id	sid	tid	token	lemma	pos
ajst-5302	1	1	academic	academic	ADJ
ajst-5302	1	2	journal	journal	NOUN
ajst-5302	1	3	of	of	ADP
ajst-5302	1	4	science	science	NOUN
ajst-5302	1	5	and	and	CCONJ
ajst-5302	1	6	technology	technology	NOUN
ajst-5302	1	7	issn	issn	NOUN
ajst-5302	1	8	:	:	PUNCT
ajst-5302	1	9	2771	2771	NUM
ajst-5302	1	10	-	-	SYM
ajst-5302	1	11	3032	3032	NUM
ajst-5302	1	12	|	|	NOUN
ajst-5302	1	13	vol	vol	NOUN
ajst-5302	1	14	.	.	PROPN
ajst-5302	2	1	5	5	NUM
ajst-5302	2	2	,	,	PUNCT
ajst-5302	2	3	no	no	INTJ
ajst-5302	2	4	.	.	NOUN
ajst-5302	2	5	1	1	NUM
ajst-5302	2	6	,	,	PUNCT
ajst-5302	2	7	2023	2023	NUM
ajst-5302	2	8	38	38	NUM
ajst-5302	2	9	vehicle	vehicle	NOUN
ajst-5302	2	10	object	object	NOUN
ajst-5302	2	11	detection	detection	NOUN
ajst-5302	2	12	based	base	VERB
ajst-5302	2	13	on	on	ADP
ajst-5302	2	14	deep	deep	ADJ
ajst-5302	2	15	learning	learning	NOUN
ajst-5302	2	16	zhaoming	zhaome	VERB
ajst-5302	2	17	zhou	zhou	PROPN
ajst-5302	2	18	,	,	PUNCT
ajst-5302	2	19	hui	hui	PROPN
ajst-5302	2	20	li	li	PROPN
ajst-5302	2	21	school	school	PROPN
ajst-5302	2	22	of	of	ADP
ajst-5302	2	23	mechatronic	mechatronic	ADJ
ajst-5302	2	24	engineering	engineering	NOUN
ajst-5302	2	25	,	,	PUNCT
ajst-5302	2	26	southwest	southwest	ADJ
ajst-5302	2	27	petroleum	petroleum	PROPN
ajst-5302	2	28	university	university	PROPN
ajst-5302	2	29	,	,	PUNCT
ajst-5302	2	30	chengdu	chengdu	PROPN
ajst-5302	2	31	610500	610500	NUM
ajst-5302	2	32	,	,	PUNCT
ajst-5302	2	33	china	china	PROPN
ajst-5302	2	34	abstract	abstract	PROPN
ajst-5302	2	35	:	:	PUNCT
ajst-5302	2	36	with	with	ADP
ajst-5302	2	37	the	the	DET
ajst-5302	2	38	continuous	continuous	ADJ
ajst-5302	2	39	improvement	improvement	NOUN
ajst-5302	2	40	of	of	ADP
ajst-5302	2	41	science	science	NOUN
ajst-5302	2	42	and	and	CCONJ
ajst-5302	2	43	technology	technology	NOUN
ajst-5302	2	44	and	and	CCONJ
ajst-5302	2	45	living	living	NOUN
ajst-5302	2	46	standards	standard	NOUN
ajst-5302	2	47	,	,	PUNCT
ajst-5302	2	48	cars	car	NOUN
ajst-5302	2	49	have	have	AUX
ajst-5302	2	50	become	become	VERB
ajst-5302	2	51	a	a	DET
ajst-5302	2	52	necessary	necessary	ADJ
ajst-5302	2	53	means	mean	NOUN
ajst-5302	2	54	of	of	ADP
ajst-5302	2	55	transportation	transportation	NOUN
ajst-5302	2	56	for	for	SCONJ
ajst-5302	2	57	people	people	NOUN
ajst-5302	2	58	to	to	PART
ajst-5302	2	59	travel	travel	VERB
ajst-5302	2	60	,	,	PUNCT
ajst-5302	2	61	which	which	PRON
ajst-5302	2	62	is	be	AUX
ajst-5302	2	63	bound	bind	VERB
ajst-5302	2	64	to	to	PART
ajst-5302	2	65	be	be	AUX
ajst-5302	2	66	followed	follow	VERB
ajst-5302	2	67	by	by	ADP
ajst-5302	2	68	traffic	traffic	NOUN
ajst-5302	2	69	accidents	accident	NOUN
ajst-5302	2	70	,	,	PUNCT
ajst-5302	2	71	so	so	CCONJ
ajst-5302	2	72	it	it	PRON
ajst-5302	2	73	is	be	AUX
ajst-5302	2	74	particularly	particularly	ADV
ajst-5302	2	75	necessary	necessary	ADJ
ajst-5302	2	76	to	to	PART
ajst-5302	2	77	detect	detect	VERB
ajst-5302	2	78	or	or	CCONJ
ajst-5302	2	79	identify	identify	VERB
ajst-5302	2	80	cars	car	NOUN
ajst-5302	2	81	.	.	PUNCT
ajst-5302	3	1	aiming	aim	VERB
ajst-5302	3	2	at	at	ADP
ajst-5302	3	3	the	the	DET
ajst-5302	3	4	above	above	ADJ
ajst-5302	3	5	problems	problem	NOUN
ajst-5302	3	6	,	,	PUNCT
ajst-5302	3	7	this	this	DET
ajst-5302	3	8	paper	paper	NOUN
ajst-5302	3	9	focuses	focus	VERB
ajst-5302	3	10	on	on	ADP
ajst-5302	3	11	the	the	DET
ajst-5302	3	12	application	application	NOUN
ajst-5302	3	13	of	of	ADP
ajst-5302	3	14	deep	deep	ADJ
ajst-5302	3	15	learning	learning	NOUN
ajst-5302	3	16	in	in	ADP
ajst-5302	3	17	vehicle	vehicle	NOUN
ajst-5302	3	18	recognition	recognition	NOUN
ajst-5302	3	19	problems	problem	NOUN
ajst-5302	3	20	,	,	PUNCT
ajst-5302	3	21	and	and	CCONJ
ajst-5302	3	22	is	be	AUX
ajst-5302	3	23	committed	commit	VERB
ajst-5302	3	24	to	to	ADP
ajst-5302	3	25	finding	find	VERB
ajst-5302	3	26	a	a	DET
ajst-5302	3	27	vehicle	vehicle	NOUN
ajst-5302	3	28	recognition	recognition	NOUN
ajst-5302	3	29	algorithm	algorithm	NOUN
ajst-5302	3	30	with	with	ADP
ajst-5302	3	31	high	high	ADJ
ajst-5302	3	32	recognition	recognition	NOUN
ajst-5302	3	33	rate	rate	NOUN
ajst-5302	3	34	and	and	CCONJ
ajst-5302	3	35	stability	stability	NOUN
ajst-5302	3	36	.	.	PUNCT
ajst-5302	4	1	this	this	DET
ajst-5302	4	2	paper	paper	NOUN
ajst-5302	4	3	focuses	focus	VERB
ajst-5302	4	4	on	on	ADP
ajst-5302	4	5	the	the	DET
ajst-5302	4	6	analysis	analysis	NOUN
ajst-5302	4	7	of	of	ADP
ajst-5302	4	8	ssd	ssd	ADJ
ajst-5302	4	9	algorithm	algorithm	NOUN
ajst-5302	4	10	and	and	CCONJ
ajst-5302	4	11	its	its	PRON
ajst-5302	4	12	basic	basic	ADJ
ajst-5302	4	13	theory	theory	NOUN
ajst-5302	4	14	convolutional	convolutional	ADJ
ajst-5302	4	15	neural	neural	ADJ
ajst-5302	4	16	network	network	NOUN
ajst-5302	4	17	.	.	PUNCT
ajst-5302	5	1	finally	finally	ADV
ajst-5302	5	2	,	,	PUNCT
ajst-5302	5	3	this	this	DET
ajst-5302	5	4	paper	paper	NOUN
ajst-5302	5	5	uses	use	VERB
ajst-5302	5	6	the	the	DET
ajst-5302	5	7	pictures	picture	NOUN
ajst-5302	5	8	of	of	ADP
ajst-5302	5	9	stationary	stationary	ADJ
ajst-5302	5	10	vehicles	vehicle	NOUN
ajst-5302	5	11	and	and	CCONJ
ajst-5302	5	12	a	a	DET
ajst-5302	5	13	film	film	NOUN
ajst-5302	5	14	video	video	NOUN
ajst-5302	5	15	respectively	respectively	ADV
ajst-5302	5	16	to	to	PART
ajst-5302	5	17	identify	identify	VERB
ajst-5302	5	18	the	the	DET
ajst-5302	5	19	stationary	stationary	ADJ
ajst-5302	5	20	and	and	CCONJ
ajst-5302	5	21	moving	move	VERB
ajst-5302	5	22	state	state	NOUN
ajst-5302	5	23	of	of	ADP
ajst-5302	5	24	vehicles	vehicle	NOUN
ajst-5302	5	25	.	.	PUNCT
ajst-5302	6	1	in	in	ADP
ajst-5302	6	2	the	the	DET
ajst-5302	6	3	process	process	NOUN
ajst-5302	6	4	of	of	ADP
ajst-5302	6	5	image	image	NOUN
ajst-5302	6	6	recognition	recognition	NOUN
ajst-5302	6	7	,	,	PUNCT
ajst-5302	6	8	the	the	DET
ajst-5302	6	9	rationality	rationality	NOUN
ajst-5302	6	10	and	and	CCONJ
ajst-5302	6	11	accuracy	accuracy	NOUN
ajst-5302	6	12	of	of	ADP
ajst-5302	6	13	the	the	DET
ajst-5302	6	14	proposed	propose	VERB
ajst-5302	6	15	method	method	NOUN
ajst-5302	6	16	are	be	AUX
ajst-5302	6	17	verified	verify	VERB
ajst-5302	6	18	according	accord	VERB
ajst-5302	6	19	to	to	ADP
ajst-5302	6	20	the	the	DET
ajst-5302	6	21	accuracy	accuracy	NOUN
ajst-5302	6	22	rate	rate	NOUN
ajst-5302	6	23	given	give	VERB
ajst-5302	6	24	above	above	ADP
ajst-5302	6	25	the	the	DET
ajst-5302	6	26	image	image	NOUN
ajst-5302	6	27	.	.	PUNCT
ajst-5302	7	1	keywords	keyword	NOUN
ajst-5302	7	2	:	:	PUNCT
ajst-5302	7	3	deep	deep	ADJ
ajst-5302	7	4	learning	learning	NOUN
ajst-5302	7	5	,	,	PUNCT
ajst-5302	7	6	vehicle	vehicle	NOUN
ajst-5302	7	7	detection	detection	NOUN
ajst-5302	7	8	,	,	PUNCT
ajst-5302	7	9	opencv	opencv	PROPN
ajst-5302	7	10	,	,	PUNCT
ajst-5302	7	11	ssd	ssd	NOUN
ajst-5302	7	12	algorithm	algorithm	NOUN
ajst-5302	7	13	.	.	PUNCT
ajst-5302	8	1	1	1	X
ajst-5302	8	2	.	.	X
ajst-5302	8	3	introduction	introduction	NOUN
ajst-5302	8	4	with	with	ADP
ajst-5302	8	5	the	the	DET
ajst-5302	8	6	continuous	continuous	ADJ
ajst-5302	8	7	development	development	NOUN
ajst-5302	8	8	of	of	ADP
ajst-5302	8	9	science	science	NOUN
ajst-5302	8	10	and	and	CCONJ
ajst-5302	8	11	technology	technology	NOUN
ajst-5302	8	12	,	,	PUNCT
ajst-5302	8	13	cars	car	NOUN
ajst-5302	8	14	have	have	AUX
ajst-5302	8	15	become	become	VERB
ajst-5302	8	16	an	an	DET
ajst-5302	8	17	indispensable	indispensable	ADJ
ajst-5302	8	18	means	mean	NOUN
ajst-5302	8	19	of	of	ADP
ajst-5302	8	20	transportation	transportation	NOUN
ajst-5302	8	21	in	in	ADP
ajst-5302	8	22	people	people	NOUN
ajst-5302	8	23	's	's	PART
ajst-5302	8	24	daily	daily	ADJ
ajst-5302	8	25	life	life	NOUN
ajst-5302	8	26	,	,	PUNCT
ajst-5302	8	27	which	which	PRON
ajst-5302	8	28	is	be	AUX
ajst-5302	8	29	also	also	ADV
ajst-5302	8	30	the	the	DET
ajst-5302	8	31	fundamental	fundamental	ADJ
ajst-5302	8	32	cause	cause	NOUN
ajst-5302	8	33	of	of	ADP
ajst-5302	8	34	traffic	traffic	NOUN
ajst-5302	8	35	congestion	congestion	NOUN
ajst-5302	8	36	and	and	CCONJ
ajst-5302	8	37	a	a	DET
ajst-5302	8	38	necessary	necessary	ADJ
ajst-5302	8	39	factor	factor	NOUN
ajst-5302	8	40	for	for	ADP
ajst-5302	8	41	traffic	traffic	NOUN
ajst-5302	8	42	accidents	accident	NOUN
ajst-5302	8	43	.	.	PUNCT
ajst-5302	9	1	it	it	PRON
ajst-5302	9	2	is	be	AUX
ajst-5302	9	3	the	the	DET
ajst-5302	9	4	existence	existence	NOUN
ajst-5302	9	5	of	of	ADP
ajst-5302	9	6	these	these	DET
ajst-5302	9	7	two	two	NUM
ajst-5302	9	8	situations	situation	NOUN
ajst-5302	9	9	that	that	PRON
ajst-5302	9	10	increase	increase	VERB
ajst-5302	9	11	the	the	DET
ajst-5302	9	12	work	work	NOUN
ajst-5302	9	13	burden	burden	NOUN
ajst-5302	9	14	of	of	ADP
ajst-5302	9	15	traffic	traffic	NOUN
ajst-5302	9	16	police	police	NOUN
ajst-5302	9	17	and	and	CCONJ
ajst-5302	9	18	related	related	ADJ
ajst-5302	9	19	units	unit	NOUN
ajst-5302	9	20	.	.	PUNCT
ajst-5302	10	1	beyond	beyond	ADP
ajst-5302	10	2	that	that	PRON
ajst-5302	10	3	,	,	PUNCT
ajst-5302	10	4	tracking	track	VERB
ajst-5302	10	5	vehicles	vehicle	NOUN
ajst-5302	10	6	is	be	AUX
ajst-5302	10	7	the	the	DET
ajst-5302	10	8	most	most	ADV
ajst-5302	10	9	immediate	immediate	ADJ
ajst-5302	10	10	problem	problem	NOUN
ajst-5302	10	11	facing	face	VERB
ajst-5302	10	12	government	government	NOUN
ajst-5302	10	13	agencies	agency	NOUN
ajst-5302	10	14	.	.	PUNCT
ajst-5302	11	1	in	in	ADP
ajst-5302	11	2	order	order	NOUN
ajst-5302	11	3	to	to	PART
ajst-5302	11	4	alleviate	alleviate	VERB
ajst-5302	11	5	this	this	DET
ajst-5302	11	6	burden	burden	NOUN
ajst-5302	11	7	and	and	CCONJ
ajst-5302	11	8	even	even	ADV
ajst-5302	11	9	eliminate	eliminate	VERB
ajst-5302	11	10	all	all	DET
ajst-5302	11	11	kinds	kind	NOUN
ajst-5302	11	12	of	of	ADP
ajst-5302	11	13	possible	possible	ADJ
ajst-5302	11	14	problems	problem	NOUN
ajst-5302	11	15	,	,	PUNCT
ajst-5302	11	16	necessary	necessary	ADJ
ajst-5302	11	17	intelligent	intelligent	ADJ
ajst-5302	11	18	monitoring	monitoring	NOUN
ajst-5302	11	19	of	of	ADP
ajst-5302	11	20	vehicles	vehicle	NOUN
ajst-5302	11	21	has	have	AUX
ajst-5302	11	22	become	become	VERB
ajst-5302	11	23	an	an	DET
ajst-5302	11	24	effective	effective	ADJ
ajst-5302	11	25	means	mean	NOUN
ajst-5302	11	26	.	.	PUNCT
ajst-5302	12	1	deep	deep	ADJ
ajst-5302	12	2	learning	learn	VERB
ajst-5302	12	3	not	not	PART
ajst-5302	12	4	only	only	ADV
ajst-5302	12	5	solves	solve	VERB
ajst-5302	12	6	these	these	DET
ajst-5302	12	7	problems	problem	NOUN
ajst-5302	12	8	,	,	PUNCT
ajst-5302	12	9	but	but	CCONJ
ajst-5302	12	10	is	be	AUX
ajst-5302	12	11	now	now	ADV
ajst-5302	12	12	being	be	AUX
ajst-5302	12	13	applied	apply	VERB
ajst-5302	12	14	to	to	ADP
ajst-5302	12	15	every	every	DET
ajst-5302	12	16	aspect	aspect	NOUN
ajst-5302	12	17	of	of	ADP
ajst-5302	12	18	life	life	NOUN
ajst-5302	12	19	.	.	PUNCT
ajst-5302	13	1	deep	deep	ADJ
ajst-5302	13	2	learning	learning	NOUN
ajst-5302	13	3	was	be	AUX
ajst-5302	13	4	first	first	ADV
ajst-5302	13	5	proposed	propose	VERB
ajst-5302	13	6	by	by	ADP
ajst-5302	13	7	hinton[1	hinton[1	PROPN
ajst-5302	13	8	]	]	X
ajst-5302	13	9	in	in	ADP
ajst-5302	13	10	2006	2006	NUM
ajst-5302	13	11	.	.	PUNCT
ajst-5302	14	1	since	since	SCONJ
ajst-5302	14	2	then	then	ADV
ajst-5302	14	3	,	,	PUNCT
ajst-5302	14	4	it	it	PRON
ajst-5302	14	5	has	have	AUX
ajst-5302	14	6	played	play	VERB
ajst-5302	14	7	an	an	DET
ajst-5302	14	8	irreplaceable	irreplaceable	ADJ
ajst-5302	14	9	role	role	NOUN
ajst-5302	14	10	in	in	ADP
ajst-5302	14	11	people	people	NOUN
ajst-5302	14	12	's	's	PART
ajst-5302	14	13	daily	daily	ADJ
ajst-5302	14	14	life	life	NOUN
ajst-5302	14	15	.	.	PUNCT
ajst-5302	15	1	therefore	therefore	ADV
ajst-5302	15	2	,	,	PUNCT
ajst-5302	15	3	scholars	scholar	NOUN
ajst-5302	15	4	at	at	ADP
ajst-5302	15	5	home	home	ADV
ajst-5302	15	6	and	and	CCONJ
ajst-5302	15	7	abroad	abroad	ADV
ajst-5302	15	8	have	have	AUX
ajst-5302	15	9	conducted	conduct	VERB
ajst-5302	15	10	extensive	extensive	ADJ
ajst-5302	15	11	research	research	NOUN
ajst-5302	15	12	on	on	ADP
ajst-5302	15	13	the	the	DET
ajst-5302	15	14	theoretical	theoretical	ADJ
ajst-5302	15	15	work	work	NOUN
ajst-5302	15	16	and	and	CCONJ
ajst-5302	15	17	application	application	NOUN
ajst-5302	15	18	objects	object	NOUN
ajst-5302	15	19	of	of	ADP
ajst-5302	15	20	deep	deep	ADJ
ajst-5302	15	21	learning	learning	NOUN
ajst-5302	15	22	.	.	PUNCT
ajst-5302	16	1	hayit	hayit	PROPN
ajst-5302	16	2	greenspan	greenspan	PROPN
ajst-5302	16	3	et	et	PROPN
ajst-5302	16	4	al.[2	al.[2	PROPN
ajst-5302	16	5	]	]	PUNCT
ajst-5302	16	6	take	take	VERB
ajst-5302	16	7	into	into	ADP
ajst-5302	16	8	account	account	NOUN
ajst-5302	16	9	the	the	DET
ajst-5302	16	10	advanced	advanced	ADJ
ajst-5302	16	11	nature	nature	NOUN
ajst-5302	16	12	of	of	ADP
ajst-5302	16	13	deep	deep	ADJ
ajst-5302	16	14	learning	learning	NOUN
ajst-5302	16	15	and	and	CCONJ
ajst-5302	16	16	realize	realize	VERB
ajst-5302	16	17	the	the	DET
ajst-5302	16	18	power	power	NOUN
ajst-5302	16	19	of	of	ADP
ajst-5302	16	20	convolutional	convolutional	ADJ
ajst-5302	16	21	neural	neural	ADJ
ajst-5302	16	22	network	network	NOUN
ajst-5302	16	23	(	(	PUNCT
ajst-5302	16	24	cnn	cnn	PROPN
ajst-5302	16	25	)	)	PUNCT
ajst-5302	16	26	in	in	ADP
ajst-5302	16	27	processing	process	VERB
ajst-5302	16	28	visual	visual	ADJ
ajst-5302	16	29	tasks	task	NOUN
ajst-5302	16	30	,	,	PUNCT
ajst-5302	16	31	so	so	SCONJ
ajst-5302	16	32	they	they	PRON
ajst-5302	16	33	use	use	VERB
ajst-5302	16	34	cnn	cnn	PROPN
ajst-5302	16	35	to	to	PART
ajst-5302	16	36	study	study	VERB
ajst-5302	16	37	the	the	DET
ajst-5302	16	38	positioning	positioning	NOUN
ajst-5302	16	39	work	work	NOUN
ajst-5302	16	40	of	of	ADP
ajst-5302	16	41	deep	deep	ADJ
ajst-5302	16	42	learning	learning	NOUN
ajst-5302	16	43	in	in	ADP
ajst-5302	16	44	medical	medical	ADJ
ajst-5302	16	45	image	image	NOUN
ajst-5302	16	46	processing	processing	NOUN
ajst-5302	16	47	.	.	PUNCT
ajst-5302	17	1	it	it	PRON
ajst-5302	17	2	also	also	ADV
ajst-5302	17	3	provides	provide	VERB
ajst-5302	17	4	some	some	DET
ajst-5302	17	5	help	help	NOUN
ajst-5302	17	6	for	for	ADP
ajst-5302	17	7	the	the	DET
ajst-5302	17	8	future	future	ADJ
ajst-5302	17	9	medical	medical	ADJ
ajst-5302	17	10	image	image	NOUN
ajst-5302	17	11	processing	processing	NOUN
ajst-5302	17	12	and	and	CCONJ
ajst-5302	17	13	the	the	DET
ajst-5302	17	14	development	development	NOUN
ajst-5302	17	15	of	of	ADP
ajst-5302	17	16	medical	medical	ADJ
ajst-5302	17	17	career	career	NOUN
ajst-5302	17	18	.	.	PUNCT
ajst-5302	18	1	amodei	amodei	PROPN
ajst-5302	18	2	d	d	X
ajst-5302	18	3	et	et	PROPN
ajst-5302	18	4	al.[3	al.[3	NOUN
ajst-5302	18	5	]	]	PUNCT
ajst-5302	18	6	replaced	replace	VERB
ajst-5302	18	7	the	the	DET
ajst-5302	18	8	entire	entire	ADJ
ajst-5302	18	9	pipeline	pipeline	NOUN
ajst-5302	18	10	of	of	ADP
ajst-5302	18	11	hand	hand	NOUN
ajst-5302	18	12	-	-	PUNCT
ajst-5302	18	13	designed	design	VERB
ajst-5302	18	14	components	component	NOUN
ajst-5302	18	15	with	with	ADP
ajst-5302	18	16	neural	neural	ADJ
ajst-5302	18	17	networks	network	NOUN
ajst-5302	18	18	,	,	PUNCT
ajst-5302	18	19	and	and	CCONJ
ajst-5302	18	20	end	end	NOUN
ajst-5302	18	21	-	-	PUNCT
ajst-5302	18	22	to	to	ADP
ajst-5302	18	23	-	-	PUNCT
ajst-5302	18	24	end	end	NOUN
ajst-5302	18	25	learning	learning	NOUN
ajst-5302	18	26	enabled	enable	VERB
ajst-5302	18	27	us	we	PRON
ajst-5302	18	28	to	to	PART
ajst-5302	18	29	process	process	VERB
ajst-5302	18	30	a	a	DET
ajst-5302	18	31	wide	wide	ADJ
ajst-5302	18	32	variety	variety	NOUN
ajst-5302	18	33	of	of	ADP
ajst-5302	18	34	speech	speech	NOUN
ajst-5302	18	35	,	,	PUNCT
ajst-5302	18	36	including	include	VERB
ajst-5302	18	37	noisy	noisy	ADJ
ajst-5302	18	38	environments	environment	NOUN
ajst-5302	18	39	,	,	PUNCT
ajst-5302	18	40	accents	accent	NOUN
ajst-5302	18	41	,	,	PUNCT
ajst-5302	18	42	and	and	CCONJ
ajst-5302	18	43	different	different	ADJ
ajst-5302	18	44	languages	language	NOUN
ajst-5302	18	45	.	.	PUNCT
ajst-5302	19	1	finally	finally	ADV
ajst-5302	19	2	,	,	PUNCT
ajst-5302	19	3	this	this	DET
ajst-5302	19	4	method	method	NOUN
ajst-5302	19	5	is	be	AUX
ajst-5302	19	6	used	use	VERB
ajst-5302	19	7	to	to	PART
ajst-5302	19	8	recognize	recognize	VERB
ajst-5302	19	9	two	two	NUM
ajst-5302	19	10	distinct	distinct	ADJ
ajst-5302	19	11	speech	speech	NOUN
ajst-5302	19	12	sounds	sound	NOUN
ajst-5302	19	13	of	of	ADP
ajst-5302	19	14	english	english	PROPN
ajst-5302	19	15	and	and	CCONJ
ajst-5302	19	16	mandarin	mandarin	PROPN
ajst-5302	19	17	,	,	PUNCT
ajst-5302	19	18	which	which	PRON
ajst-5302	19	19	opens	open	VERB
ajst-5302	19	20	a	a	DET
ajst-5302	19	21	successful	successful	ADJ
ajst-5302	19	22	door	door	NOUN
ajst-5302	19	23	for	for	ADP
ajst-5302	19	24	future	future	ADJ
ajst-5302	19	25	speech	speech	NOUN
ajst-5302	19	26	recognition	recognition	NOUN
ajst-5302	19	27	work	work	NOUN
ajst-5302	19	28	.	.	PUNCT
ajst-5302	20	1	finally	finally	ADV
ajst-5302	20	2	,	,	PUNCT
ajst-5302	20	3	looking	look	VERB
ajst-5302	20	4	at	at	ADP
ajst-5302	20	5	the	the	DET
ajst-5302	20	6	advantages	advantage	NOUN
ajst-5302	20	7	of	of	ADP
ajst-5302	20	8	deep	deep	ADJ
ajst-5302	20	9	learning	learning	NOUN
ajst-5302	20	10	in	in	ADP
ajst-5302	20	11	natural	natural	ADJ
ajst-5302	20	12	language	language	NOUN
ajst-5302	20	13	processing	processing	NOUN
ajst-5302	20	14	,	,	PUNCT
ajst-5302	20	15	american	american	PROPN
ajst-5302	20	16	scholar	scholar	PROPN
ajst-5302	20	17	ronan	ronan	PROPN
ajst-5302	20	18	collobert	collobert	VERB
ajst-5302	20	19	et	et	PROPN
ajst-5302	20	20	al.[4	al.[4	PROPN
ajst-5302	20	21	]	]	PUNCT
ajst-5302	20	22	described	describe	VERB
ajst-5302	20	23	a	a	DET
ajst-5302	20	24	single	single	ADJ
ajst-5302	20	25	convolutional	convolutional	ADJ
ajst-5302	20	26	neural	neural	ADJ
ajst-5302	20	27	network	network	NOUN
ajst-5302	20	28	architecture	architecture	NOUN
ajst-5302	20	29	that	that	PRON
ajst-5302	20	30	,	,	PUNCT
ajst-5302	20	31	given	give	VERB
ajst-5302	20	32	a	a	DET
ajst-5302	20	33	sentence	sentence	NOUN
ajst-5302	20	34	,	,	PUNCT
ajst-5302	20	35	outputs	output	VERB
ajst-5302	20	36	a	a	DET
ajst-5302	20	37	series	series	NOUN
ajst-5302	20	38	of	of	ADP
ajst-5302	20	39	language	language	NOUN
ajst-5302	20	40	processing	processing	NOUN
ajst-5302	20	41	predictions	prediction	NOUN
ajst-5302	20	42	:	:	PUNCT
ajst-5302	20	43	part	part	NOUN
ajst-5302	20	44	of	of	ADP
ajst-5302	20	45	speech	speech	NOUN
ajst-5302	20	46	tags	tag	NOUN
ajst-5302	20	47	,	,	PUNCT
ajst-5302	20	48	blocks	block	NOUN
ajst-5302	20	49	,	,	PUNCT
ajst-5302	20	50	named	name	VERB
ajst-5302	20	51	entity	entity	NOUN
ajst-5302	20	52	tags	tag	NOUN
ajst-5302	20	53	,	,	PUNCT
ajst-5302	20	54	semantic	semantic	ADJ
ajst-5302	20	55	roles	role	NOUN
ajst-5302	20	56	,	,	PUNCT
ajst-5302	20	57	the	the	DET
ajst-5302	20	58	likelihood	likelihood	NOUN
ajst-5302	20	59	that	that	SCONJ
ajst-5302	20	60	semantically	semantically	ADV
ajst-5302	20	61	similar	similar	ADJ
ajst-5302	20	62	words	word	NOUN
ajst-5302	20	63	and	and	CCONJ
ajst-5302	20	64	sentences	sentence	NOUN
ajst-5302	20	65	will	will	AUX
ajst-5302	20	66	have	have	AUX
ajst-5302	20	67	meaning	mean	VERB
ajst-5302	20	68	.	.	PUNCT
ajst-5302	21	1	the	the	DET
ajst-5302	21	2	entire	entire	ADJ
ajst-5302	21	3	network	network	NOUN
ajst-5302	21	4	uses	use	VERB
ajst-5302	21	5	re	re	NOUN
ajst-5302	21	6	-	-	NOUN
ajst-5302	21	7	sharing	sharing	ADJ
ajst-5302	21	8	(	(	PUNCT
ajst-5302	21	9	instances	instance	NOUN
ajst-5302	21	10	of	of	ADP
ajst-5302	21	11	multitasking	multitaske	VERB
ajst-5302	21	12	learning	learning	NOUN
ajst-5302	21	13	)	)	PUNCT
ajst-5302	21	14	to	to	PART
ajst-5302	21	15	train	train	VERB
ajst-5302	21	16	all	all	PRON
ajst-5302	21	17	of	of	ADP
ajst-5302	21	18	these	these	DET
ajst-5302	21	19	tasks	task	NOUN
ajst-5302	21	20	together	together	ADV
ajst-5302	21	21	.	.	PUNCT
ajst-5302	22	1	domestic	domestic	ADJ
ajst-5302	22	2	research	research	NOUN
ajst-5302	22	3	on	on	ADP
ajst-5302	22	4	deep	deep	ADJ
ajst-5302	22	5	learning	learning	NOUN
ajst-5302	22	6	has	have	AUX
ajst-5302	22	7	also	also	ADV
ajst-5302	22	8	made	make	VERB
ajst-5302	22	9	great	great	ADJ
ajst-5302	22	10	progress	progress	NOUN
ajst-5302	22	11	and	and	CCONJ
ajst-5302	22	12	obtained	obtain	VERB
ajst-5302	22	13	a	a	DET
ajst-5302	22	14	lot	lot	NOUN
ajst-5302	22	15	of	of	ADP
ajst-5302	22	16	research	research	NOUN
ajst-5302	22	17	results	result	NOUN
ajst-5302	22	18	.	.	PUNCT
ajst-5302	23	1	ding	de	VERB
ajst-5302	23	2	hong	hong	PROPN
ajst-5302	23	3	and	and	CCONJ
ajst-5302	23	4	rao	rao	NOUN
ajst-5302	23	5	wanxian[5	wanxian[5	PRON
ajst-5302	23	6	]	]	PUNCT
ajst-5302	23	7	used	use	VERB
ajst-5302	23	8	the	the	DET
ajst-5302	23	9	deep	deep	ADJ
ajst-5302	23	10	belief	belief	NOUN
ajst-5302	23	11	network	network	NOUN
ajst-5302	23	12	to	to	PART
ajst-5302	23	13	detect	detect	VERB
ajst-5302	23	14	human	human	ADJ
ajst-5302	23	15	behavior	behavior	NOUN
ajst-5302	23	16	.	.	PUNCT
ajst-5302	24	1	before	before	ADP
ajst-5302	24	2	using	use	VERB
ajst-5302	24	3	the	the	DET
ajst-5302	24	4	deep	deep	ADJ
ajst-5302	24	5	belief	belief	NOUN
ajst-5302	24	6	network	network	NOUN
ajst-5302	24	7	for	for	ADP
ajst-5302	24	8	recognition	recognition	NOUN
ajst-5302	24	9	,	,	PUNCT
ajst-5302	24	10	the	the	DET
ajst-5302	24	11	author	author	NOUN
ajst-5302	24	12	preprocessed	preprocesse	VERB
ajst-5302	24	13	the	the	DET
ajst-5302	24	14	data	datum	NOUN
ajst-5302	24	15	,	,	PUNCT
ajst-5302	24	16	denoised	denoise	VERB
ajst-5302	24	17	the	the	DET
ajst-5302	24	18	data	datum	NOUN
ajst-5302	24	19	using	use	VERB
ajst-5302	24	20	wavelet	wavelet	NOUN
ajst-5302	24	21	technology	technology	NOUN
ajst-5302	24	22	,	,	PUNCT
ajst-5302	24	23	and	and	CCONJ
ajst-5302	24	24	then	then	ADV
ajst-5302	24	25	used	use	VERB
ajst-5302	24	26	pca	pca	PROPN
ajst-5302	24	27	method	method	NOUN
ajst-5302	24	28	for	for	ADP
ajst-5302	24	29	main	main	ADJ
ajst-5302	24	30	frequency	frequency	NOUN
ajst-5302	24	31	analysis	analysis	NOUN
ajst-5302	24	32	.	.	PUNCT
ajst-5302	25	1	finally	finally	ADV
ajst-5302	25	2	,	,	PUNCT
ajst-5302	25	3	the	the	DET
ajst-5302	25	4	processed	process	VERB
ajst-5302	25	5	data	datum	NOUN
ajst-5302	25	6	was	be	AUX
ajst-5302	25	7	substituted	substitute	VERB
ajst-5302	25	8	into	into	ADP
ajst-5302	25	9	the	the	DET
ajst-5302	25	10	deep	deep	ADJ
ajst-5302	25	11	belief	belief	NOUN
ajst-5302	25	12	network	network	NOUN
ajst-5302	25	13	for	for	ADP
ajst-5302	25	14	training	training	NOUN
ajst-5302	25	15	,	,	PUNCT
ajst-5302	25	16	and	and	CCONJ
ajst-5302	25	17	the	the	DET
ajst-5302	25	18	model	model	NOUN
ajst-5302	25	19	was	be	AUX
ajst-5302	25	20	obtained	obtain	VERB
ajst-5302	25	21	for	for	ADP
ajst-5302	25	22	human	human	ADJ
ajst-5302	25	23	behavior	behavior	NOUN
ajst-5302	25	24	recognition	recognition	NOUN
ajst-5302	25	25	.	.	PUNCT
ajst-5302	26	1	the	the	DET
ajst-5302	26	2	deep	deep	ADJ
ajst-5302	26	3	belief	belief	NOUN
ajst-5302	26	4	network	network	NOUN
ajst-5302	26	5	proposed	propose	VERB
ajst-5302	26	6	in	in	ADP
ajst-5302	26	7	this	this	DET
ajst-5302	26	8	paper	paper	NOUN
ajst-5302	26	9	is	be	AUX
ajst-5302	26	10	as	as	ADV
ajst-5302	26	11	effective	effective	ADJ
ajst-5302	26	12	as	as	ADP
ajst-5302	26	13	other	other	ADJ
ajst-5302	26	14	machine	machine	NOUN
ajst-5302	26	15	learning	learning	NOUN
ajst-5302	26	16	methods	method	NOUN
ajst-5302	26	17	.	.	PUNCT
ajst-5302	27	1	in	in	ADP
ajst-5302	27	2	order	order	NOUN
ajst-5302	27	3	to	to	PART
ajst-5302	27	4	improve	improve	VERB
ajst-5302	27	5	the	the	DET
ajst-5302	27	6	image	image	NOUN
ajst-5302	27	7	recognition	recognition	NOUN
ajst-5302	27	8	accuracy	accuracy	NOUN
ajst-5302	27	9	,	,	PUNCT
ajst-5302	27	10	gao	gao	PROPN
ajst-5302	27	11	yuan	yuan	PROPN
ajst-5302	27	12	et	et	PROPN
ajst-5302	27	13	al.[6	al.[6	PROPN
ajst-5302	27	14	]	]	PUNCT
ajst-5302	27	15	proposed	propose	VERB
ajst-5302	27	16	a	a	DET
ajst-5302	27	17	wide	wide	ADJ
ajst-5302	27	18	residual	residual	ADJ
ajst-5302	27	19	superresolution	superresolution	NOUN
ajst-5302	27	20	neural	neural	ADJ
ajst-5302	27	21	network	network	NOUN
ajst-5302	27	22	based	base	VERB
ajst-5302	27	23	on	on	ADP
ajst-5302	27	24	depth	depth	NOUN
ajst-5302	27	25	-	-	PUNCT
ajst-5302	27	26	detachability	detachability	NOUN
ajst-5302	27	27	convolution	convolution	NOUN
ajst-5302	27	28	.	.	PUNCT
ajst-5302	28	1	this	this	DET
ajst-5302	28	2	method	method	NOUN
ajst-5302	28	3	divides	divide	VERB
ajst-5302	28	4	the	the	DET
ajst-5302	28	5	channels	channel	NOUN
ajst-5302	28	6	of	of	ADP
ajst-5302	28	7	the	the	DET
ajst-5302	28	8	convolutional	convolutional	ADJ
ajst-5302	28	9	layer	layer	NOUN
ajst-5302	28	10	into	into	ADP
ajst-5302	28	11	several	several	ADJ
ajst-5302	28	12	groups	group	NOUN
ajst-5302	28	13	and	and	CCONJ
ajst-5302	28	14	normizes	normize	VERB
ajst-5302	28	15	the	the	DET
ajst-5302	28	16	data	datum	NOUN
ajst-5302	28	17	of	of	ADP
ajst-5302	28	18	each	each	DET
ajst-5302	28	19	group	group	NOUN
ajst-5302	28	20	.	.	PUNCT
ajst-5302	29	1	based	base	VERB
ajst-5302	29	2	on	on	ADP
ajst-5302	29	3	the	the	DET
ajst-5302	29	4	subject	subject	NOUN
ajst-5302	29	5	of	of	ADP
ajst-5302	29	6	vehicle	vehicle	NOUN
ajst-5302	29	7	target	target	NOUN
ajst-5302	29	8	detection	detection	NOUN
ajst-5302	29	9	,	,	PUNCT
ajst-5302	29	10	this	this	DET
ajst-5302	29	11	paper	paper	NOUN
ajst-5302	29	12	selects	select	VERB
ajst-5302	29	13	opencv	opencv	NOUN
ajst-5302	29	14	toolbox[7	toolbox[7	NUM
ajst-5302	29	15	]	]	PUNCT
ajst-5302	29	16	and	and	CCONJ
ajst-5302	29	17	uses	use	VERB
ajst-5302	29	18	the	the	DET
ajst-5302	29	19	method	method	NOUN
ajst-5302	29	20	of	of	ADP
ajst-5302	29	21	single	single	ADJ
ajst-5302	29	22	target	target	NOUN
ajst-5302	29	23	multi	multi	ADJ
ajst-5302	29	24	-	-	ADJ
ajst-5302	29	25	box	box	NOUN
ajst-5302	29	26	detection	detection	NOUN
ajst-5302	29	27	(	(	PUNCT
ajst-5302	29	28	single	single	ADJ
ajst-5302	29	29	shot	shot	NOUN
ajst-5302	29	30	multibox	multibox	NOUN
ajst-5302	29	31	detector	detector	NOUN
ajst-5302	29	32	,	,	PUNCT
ajst-5302	29	33	ssd)[8,9]combined	ssd)[8,9]combine	VERB
ajst-5302	29	34	with	with	ADP
ajst-5302	29	35	deep	deep	ADJ
ajst-5302	29	36	neural	neural	ADJ
ajst-5302	29	37	network	network	NOUN
ajst-5302	29	38	(	(	PUNCT
ajst-5302	29	39	deep	deep	ADJ
ajst-5302	29	40	neural	neural	ADJ
ajst-5302	29	41	network	network	NOUN
ajst-5302	29	42	,	,	PUNCT
ajst-5302	29	43	dnn)[10,11]to	dnn)[10,11]to	ADV
ajst-5302	29	44	achieve	achieve	VERB
ajst-5302	29	45	accurate	accurate	ADJ
ajst-5302	29	46	and	and	CCONJ
ajst-5302	29	47	efficient	efficient	ADJ
ajst-5302	29	48	vehicle	vehicle	NOUN
ajst-5302	29	49	positioning	positioning	NOUN
ajst-5302	29	50	and	and	CCONJ
ajst-5302	29	51	recognition	recognition	NOUN
ajst-5302	29	52	.	.	PUNCT
ajst-5302	30	1	2	2	X
ajst-5302	30	2	.	.	X
ajst-5302	30	3	the	the	DET
ajst-5302	30	4	theoretical	theoretical	ADJ
ajst-5302	30	5	basis	basis	NOUN
ajst-5302	30	6	of	of	ADP
ajst-5302	30	7	the	the	DET
ajst-5302	30	8	research	research	NOUN
ajst-5302	30	9	in	in	ADP
ajst-5302	30	10	this	this	DET
ajst-5302	30	11	paper	paper	NOUN
ajst-5302	30	12	,	,	PUNCT
ajst-5302	30	13	mobilenet	mobilenet	NOUN
ajst-5302	30	14	ssd	ssd	PROPN
ajst-5302	30	15	in	in	ADP
ajst-5302	30	16	opencv	opencv	PROPN
ajst-5302	30	17	and	and	CCONJ
ajst-5302	30	18	deep	deep	ADJ
ajst-5302	30	19	neural	neural	ADJ
ajst-5302	30	20	network	network	NOUN
ajst-5302	30	21	module	module	NOUN
ajst-5302	30	22	are	be	AUX
ajst-5302	30	23	combined	combine	VERB
ajst-5302	30	24	to	to	PART
ajst-5302	30	25	construct	construct	VERB
ajst-5302	30	26	vehicle	vehicle	NOUN
ajst-5302	30	27	target	target	NOUN
ajst-5302	30	28	detector	detector	NOUN
ajst-5302	30	29	.	.	PUNCT
ajst-5302	31	1	2.1	2.1	NUM
ajst-5302	31	2	.	.	PUNCT
ajst-5302	32	1	deep	deep	ADJ
ajst-5302	32	2	neural	neural	ADJ
ajst-5302	32	3	network	network	PROPN
ajst-5302	32	4	dnn	dnn	PROPN
ajst-5302	32	5	dnn	dnn	PROPN
ajst-5302	32	6	divides	divide	VERB
ajst-5302	32	7	neural	neural	ADJ
ajst-5302	32	8	network	network	NOUN
ajst-5302	32	9	layers	layer	NOUN
ajst-5302	32	10	into	into	ADP
ajst-5302	32	11	three	three	NUM
ajst-5302	32	12	categories	category	NOUN
ajst-5302	32	13	according	accord	VERB
ajst-5302	32	14	to	to	ADP
ajst-5302	32	15	the	the	DET
ajst-5302	32	16	layers	layer	NOUN
ajst-5302	32	17	in	in	ADP
ajst-5302	32	18	different	different	ADJ
ajst-5302	32	19	positions	position	NOUN
ajst-5302	32	20	,	,	PUNCT
ajst-5302	32	21	namely	namely	ADV
ajst-5302	32	22	input	input	NOUN
ajst-5302	32	23	layer	layer	NOUN
ajst-5302	32	24	,	,	PUNCT
ajst-5302	32	25	hidden	hide	VERB
ajst-5302	32	26	layer	layer	NOUN
ajst-5302	32	27	and	and	CCONJ
ajst-5302	32	28	output	output	NOUN
ajst-5302	32	29	layer	layer	NOUN
ajst-5302	32	30	.	.	PUNCT
ajst-5302	33	1	fig	fig	NOUN
ajst-5302	33	2	.	.	PUNCT
ajst-5302	34	1	1	1	NUM
ajst-5302	34	2	shows	show	VERB
ajst-5302	34	3	the	the	DET
ajst-5302	34	4	specific	specific	ADJ
ajst-5302	34	5	figure	figure	NOUN
ajst-5302	34	6	.	.	PUNCT
ajst-5302	35	1	39	39	NUM
ajst-5302	35	2	figure	figure	NOUN
ajst-5302	35	3	1	1	NUM
ajst-5302	35	4	.	.	PUNCT
ajst-5302	35	5	neural	neural	ADJ
ajst-5302	35	6	network	network	NOUN
ajst-5302	35	7	structure	structure	NOUN
ajst-5302	35	8	the	the	DET
ajst-5302	35	9	middle	middle	ADJ
ajst-5302	35	10	layer	layer	NOUN
ajst-5302	35	11	and	and	CCONJ
ajst-5302	35	12	layer	layer	NOUN
ajst-5302	35	13	of	of	ADP
ajst-5302	35	14	dnn	dnn	PROPN
ajst-5302	35	15	can	can	AUX
ajst-5302	35	16	be	be	AUX
ajst-5302	35	17	fully	fully	ADV
ajst-5302	35	18	connected	connect	VERB
ajst-5302	35	19	.	.	PUNCT
ajst-5302	36	1	specifically	specifically	ADV
ajst-5302	36	2	,	,	PUNCT
ajst-5302	36	3	the	the	DET
ajst-5302	36	4	neurons	neuron	NOUN
ajst-5302	36	5	in	in	ADP
ajst-5302	36	6	layer	layer	NOUN
ajst-5302	36	7	𝑖	𝑖	NOUN
ajst-5302	36	8	are	be	AUX
ajst-5302	36	9	connected	connect	VERB
ajst-5302	36	10	with	with	ADP
ajst-5302	36	11	each	each	DET
ajst-5302	36	12	neuron	neuron	NOUN
ajst-5302	36	13	in	in	ADP
ajst-5302	36	14	layer	layer	NOUN
ajst-5302	36	15	𝑖	𝑖	PRON
ajst-5302	36	16	1	1	NUM
ajst-5302	36	17	,	,	PUNCT
ajst-5302	36	18	and	and	CCONJ
ajst-5302	36	19	the	the	DET
ajst-5302	36	20	specific	specific	ADJ
ajst-5302	36	21	connection	connection	NOUN
ajst-5302	36	22	mode	mode	NOUN
ajst-5302	36	23	can	can	AUX
ajst-5302	36	24	be	be	AUX
ajst-5302	36	25	expressed	express	VERB
ajst-5302	36	26	as	as	ADP
ajst-5302	36	27	equation	equation	NOUN
ajst-5302	36	28	(	(	PUNCT
ajst-5302	36	29	1	1	NUM
ajst-5302	36	30	)	)	PUNCT
ajst-5302	36	31	.	.	PUNCT
ajst-5302	37	1	𝑧	𝑧	PRON
ajst-5302	37	2	∑𝑤	∑𝑤	PROPN
ajst-5302	37	3	𝑥	𝑥	X
ajst-5302	37	4	𝑏	𝑏	PROPN
ajst-5302	37	5	(	(	PUNCT
ajst-5302	37	6	1	1	NUM
ajst-5302	37	7	)	)	PUNCT
ajst-5302	37	8	in	in	ADP
ajst-5302	37	9	equation	equation	NOUN
ajst-5302	37	10	(	(	PUNCT
ajst-5302	37	11	1	1	NUM
ajst-5302	37	12	)	)	PUNCT
ajst-5302	37	13	,	,	PUNCT
ajst-5302	37	14	𝑧	𝑧	PROPN
ajst-5302	37	15	represents	represent	VERB
ajst-5302	37	16	the	the	DET
ajst-5302	37	17	output	output	NOUN
ajst-5302	37	18	of	of	ADP
ajst-5302	37	19	neurons	neuron	NOUN
ajst-5302	37	20	in	in	ADP
ajst-5302	37	21	layer	layer	NOUN
ajst-5302	37	22	𝑖	𝑖	PRON
ajst-5302	37	23	1	1	NUM
ajst-5302	37	24	,	,	PUNCT
ajst-5302	37	25	𝑤	𝑤	PART
ajst-5302	37	26	represents	represent	VERB
ajst-5302	37	27	the	the	DET
ajst-5302	37	28	weight	weight	NOUN
ajst-5302	37	29	vector	vector	NOUN
ajst-5302	37	30	of	of	ADP
ajst-5302	37	31	neurons	neuron	NOUN
ajst-5302	37	32	in	in	ADP
ajst-5302	37	33	layer	layer	NOUN
ajst-5302	37	34	𝑖	𝑖	NOUN
ajst-5302	37	35	,	,	PUNCT
ajst-5302	37	36	𝑥	𝑥	PROPN
ajst-5302	37	37	represents	represent	VERB
ajst-5302	37	38	the	the	DET
ajst-5302	37	39	value	value	NOUN
ajst-5302	37	40	of	of	ADP
ajst-5302	37	41	neurons	neuron	NOUN
ajst-5302	37	42	in	in	ADP
ajst-5302	37	43	layer	layer	NOUN
ajst-5302	37	44	𝑖	𝑖	NOUN
ajst-5302	37	45	,	,	PUNCT
ajst-5302	37	46	and	and	CCONJ
ajst-5302	37	47	𝑏	𝑏	PROPN
ajst-5302	37	48	represents	represent	VERB
ajst-5302	37	49	the	the	DET
ajst-5302	37	50	bias	bias	NOUN
ajst-5302	37	51	of	of	ADP
ajst-5302	37	52	neurons	neuron	NOUN
ajst-5302	37	53	in	in	ADP
ajst-5302	37	54	layer	layer	NOUN
ajst-5302	37	55	𝑖.	𝑖.	ADP
ajst-5302	37	56	the	the	DET
ajst-5302	37	57	definition	definition	NOUN
ajst-5302	37	58	of	of	ADP
ajst-5302	37	59	parameters	parameter	NOUN
ajst-5302	37	60	between	between	ADP
ajst-5302	37	61	neurons	neuron	NOUN
ajst-5302	37	62	in	in	ADP
ajst-5302	37	63	dnn	dnn	PROPN
ajst-5302	37	64	can	can	AUX
ajst-5302	37	65	be	be	AUX
ajst-5302	37	66	shown	show	VERB
ajst-5302	37	67	in	in	ADP
ajst-5302	37	68	fig	fig	NOUN
ajst-5302	37	69	.	.	PUNCT
ajst-5302	38	1	2	2	X
ajst-5302	38	2	.	.	X
ajst-5302	38	3	figure	figure	NOUN
ajst-5302	38	4	2	2	NUM
ajst-5302	38	5	.	.	PUNCT
ajst-5302	38	6	stealth	stealth	ADJ
ajst-5302	38	7	relationship	relationship	NOUN
ajst-5302	38	8	between	between	ADP
ajst-5302	38	9	the	the	DET
ajst-5302	38	10	two	two	NUM
ajst-5302	38	11	layers	layer	NOUN
ajst-5302	38	12	of	of	ADP
ajst-5302	38	13	neurons	neuron	NOUN
ajst-5302	38	14	it	it	PRON
ajst-5302	38	15	can	can	AUX
ajst-5302	38	16	be	be	AUX
ajst-5302	38	17	seen	see	VERB
ajst-5302	38	18	from	from	ADP
ajst-5302	38	19	the	the	DET
ajst-5302	38	20	figure	figure	NOUN
ajst-5302	38	21	that	that	SCONJ
ajst-5302	38	22	the	the	DET
ajst-5302	38	23	connection	connection	NOUN
ajst-5302	38	24	between	between	ADP
ajst-5302	38	25	the	the	DET
ajst-5302	38	26	two	two	NUM
ajst-5302	38	27	layers	layer	NOUN
ajst-5302	38	28	of	of	ADP
ajst-5302	38	29	neurons	neuron	NOUN
ajst-5302	38	30	is	be	AUX
ajst-5302	38	31	connected	connect	VERB
ajst-5302	38	32	by	by	ADP
ajst-5302	38	33	weight	weight	NOUN
ajst-5302	38	34	.	.	PUNCT
ajst-5302	39	1	in	in	ADP
ajst-5302	39	2	the	the	DET
ajst-5302	39	3	figure	figure	NOUN
ajst-5302	39	4	,	,	PUNCT
ajst-5302	39	5	𝑤	𝑤	PART
ajst-5302	39	6	represents	represent	VERB
ajst-5302	39	7	the	the	DET
ajst-5302	39	8	weight	weight	NOUN
ajst-5302	39	9	of	of	ADP
ajst-5302	39	10	the	the	DET
ajst-5302	39	11	connection	connection	NOUN
ajst-5302	39	12	between	between	ADP
ajst-5302	39	13	the	the	DET
ajst-5302	39	14	first	first	ADJ
ajst-5302	39	15	neuron	neuron	NOUN
ajst-5302	39	16	of	of	ADP
ajst-5302	39	17	the	the	DET
ajst-5302	39	18	first	first	ADJ
ajst-5302	39	19	layer	layer	NOUN
ajst-5302	39	20	and	and	CCONJ
ajst-5302	39	21	the	the	DET
ajst-5302	39	22	first	first	ADJ
ajst-5302	39	23	neuron	neuron	NOUN
ajst-5302	39	24	of	of	ADP
ajst-5302	39	25	the	the	DET
ajst-5302	39	26	next	next	ADJ
ajst-5302	39	27	layer	layer	NOUN
ajst-5302	39	28	,	,	PUNCT
ajst-5302	39	29	and	and	CCONJ
ajst-5302	39	30	𝑤	𝑤	ADP
ajst-5302	39	31	represents	represent	VERB
ajst-5302	39	32	the	the	DET
ajst-5302	39	33	weight	weight	NOUN
ajst-5302	39	34	of	of	ADP
ajst-5302	39	35	the	the	DET
ajst-5302	39	36	connection	connection	NOUN
ajst-5302	39	37	between	between	ADP
ajst-5302	39	38	the	the	DET
ajst-5302	39	39	third	third	ADJ
ajst-5302	39	40	neuron	neuron	NOUN
ajst-5302	39	41	of	of	ADP
ajst-5302	39	42	the	the	DET
ajst-5302	39	43	first	first	ADJ
ajst-5302	39	44	layer	layer	NOUN
ajst-5302	39	45	and	and	CCONJ
ajst-5302	39	46	the	the	DET
ajst-5302	39	47	fourth	fourth	ADJ
ajst-5302	39	48	neuron	neuron	NOUN
ajst-5302	39	49	of	of	ADP
ajst-5302	39	50	the	the	DET
ajst-5302	39	51	next	next	ADJ
ajst-5302	39	52	layer	layer	NOUN
ajst-5302	39	53	.	.	PUNCT
ajst-5302	40	1	take	take	VERB
ajst-5302	40	2	the	the	DET
ajst-5302	40	3	output	output	NOUN
ajst-5302	40	4	of	of	ADP
ajst-5302	40	5	the	the	DET
ajst-5302	40	6	first	first	ADJ
ajst-5302	40	7	neuron	neuron	NOUN
ajst-5302	40	8	in	in	ADP
ajst-5302	40	9	the	the	DET
ajst-5302	40	10	second	second	ADJ
ajst-5302	40	11	layer	layer	NOUN
ajst-5302	40	12	for	for	ADP
ajst-5302	40	13	example	example	NOUN
ajst-5302	40	14	,	,	PUNCT
ajst-5302	40	15	as	as	SCONJ
ajst-5302	40	16	shown	show	VERB
ajst-5302	40	17	in	in	ADP
ajst-5302	40	18	equation	equation	NOUN
ajst-5302	40	19	(	(	PUNCT
ajst-5302	40	20	2	2	NUM
ajst-5302	40	21	)	)	PUNCT
ajst-5302	40	22	.	.	PUNCT
ajst-5302	41	1	𝑧	𝑧	PRON
ajst-5302	41	2	𝑤	𝑤	PART
ajst-5302	41	3	𝑥	𝑥	NOUN
ajst-5302	41	4	𝑤	𝑤	PART
ajst-5302	41	5	𝑥	𝑥	VERB
ajst-5302	41	6	𝑤	𝑤	PART
ajst-5302	41	7	𝑥	𝑥	X
ajst-5302	41	8	𝑏	𝑏	PROPN
ajst-5302	41	9	(	(	PUNCT
ajst-5302	41	10	2	2	NUM
ajst-5302	41	11	)	)	PUNCT
ajst-5302	41	12	in	in	ADP
ajst-5302	41	13	formula	formula	NOUN
ajst-5302	41	14	(	(	PUNCT
ajst-5302	41	15	2	2	NUM
ajst-5302	41	16	)	)	PUNCT
ajst-5302	41	17	,	,	PUNCT
ajst-5302	41	18	𝑥	𝑥	X
ajst-5302	41	19	,	,	PUNCT
ajst-5302	41	20	𝑥	𝑥	PROPN
ajst-5302	41	21	and	and	CCONJ
ajst-5302	41	22	𝑥	𝑥	PROPN
ajst-5302	41	23	respectively	respectively	ADV
ajst-5302	41	24	represent	represent	VERB
ajst-5302	41	25	the	the	DET
ajst-5302	41	26	values	value	NOUN
ajst-5302	41	27	of	of	ADP
ajst-5302	41	28	the	the	DET
ajst-5302	41	29	three	three	NUM
ajst-5302	41	30	neurons	neuron	NOUN
ajst-5302	41	31	in	in	ADP
ajst-5302	41	32	the	the	DET
ajst-5302	41	33	first	first	ADJ
ajst-5302	41	34	layer	layer	NOUN
ajst-5302	41	35	,	,	PUNCT
ajst-5302	41	36	𝑏	𝑏	PROPN
ajst-5302	41	37	represents	represent	VERB
ajst-5302	41	38	the	the	DET
ajst-5302	41	39	bias	bias	NOUN
ajst-5302	41	40	of	of	ADP
ajst-5302	41	41	the	the	DET
ajst-5302	41	42	first	first	ADJ
ajst-5302	41	43	layer	layer	NOUN
ajst-5302	41	44	,	,	PUNCT
ajst-5302	41	45	and	and	CCONJ
ajst-5302	41	46	b	b	NOUN
ajst-5302	41	47	represents	represent	VERB
ajst-5302	41	48	the	the	DET
ajst-5302	41	49	weight	weight	NOUN
ajst-5302	41	50	.	.	PUNCT
ajst-5302	42	1	the	the	DET
ajst-5302	42	2	forward	forward	ADJ
ajst-5302	42	3	propagation	propagation	NOUN
ajst-5302	42	4	of	of	ADP
ajst-5302	42	5	eigenvalues	eigenvalue	NOUN
ajst-5302	42	6	in	in	ADP
ajst-5302	42	7	a	a	DET
ajst-5302	42	8	neural	neural	ADJ
ajst-5302	42	9	network	network	NOUN
ajst-5302	42	10	is	be	AUX
ajst-5302	42	11	that	that	SCONJ
ajst-5302	42	12	multiple	multiple	ADJ
ajst-5302	42	13	neurons	neuron	NOUN
ajst-5302	42	14	in	in	ADP
ajst-5302	42	15	fig	fig	NOUN
ajst-5302	42	16	.	.	PUNCT
ajst-5302	43	1	2	2	NUM
ajst-5302	43	2	are	be	AUX
ajst-5302	43	3	overlapped	overlap	VERB
ajst-5302	43	4	until	until	ADP
ajst-5302	43	5	the	the	DET
ajst-5302	43	6	final	final	ADJ
ajst-5302	43	7	output	output	NOUN
ajst-5302	43	8	.	.	PUNCT
ajst-5302	44	1	2.2	2.2	NUM
ajst-5302	44	2	.	.	PUNCT
ajst-5302	45	1	convolutional	convolutional	ADJ
ajst-5302	45	2	neural	neural	ADJ
ajst-5302	45	3	networks	network	NOUN
ajst-5302	45	4	cnn	cnn	PROPN
ajst-5302	45	5	the	the	DET
ajst-5302	45	6	classic	classic	ADJ
ajst-5302	45	7	cnn	cnn	PROPN
ajst-5302	45	8	model	model	NOUN
ajst-5302	45	9	is	be	AUX
ajst-5302	45	10	shown	show	VERB
ajst-5302	45	11	in	in	ADP
ajst-5302	45	12	fig	fig	NOUN
ajst-5302	45	13	.	.	PUNCT
ajst-5302	46	1	3	3	NUM
ajst-5302	46	2	,	,	PUNCT
ajst-5302	46	3	in	in	ADP
ajst-5302	46	4	which	which	PRON
ajst-5302	46	5	2d	2d	NOUN
ajst-5302	46	6	images	image	NOUN
ajst-5302	46	7	are	be	AUX
ajst-5302	46	8	directly	directly	ADV
ajst-5302	46	9	input	input	ADJ
ajst-5302	46	10	into	into	ADP
ajst-5302	46	11	the	the	DET
ajst-5302	46	12	network	network	NOUN
ajst-5302	46	13	and	and	CCONJ
ajst-5302	46	14	then	then	ADV
ajst-5302	46	15	convolved	convolve	VERB
ajst-5302	46	16	with	with	ADP
ajst-5302	46	17	several	several	ADJ
ajst-5302	46	18	adjustable	adjustable	ADJ
ajst-5302	46	19	convolution	convolution	NOUN
ajst-5302	46	20	kernels	kernel	NOUN
ajst-5302	46	21	to	to	PART
ajst-5302	46	22	generate	generate	VERB
ajst-5302	46	23	corresponding	corresponding	ADJ
ajst-5302	46	24	feature	feature	NOUN
ajst-5302	46	25	maps	map	NOUN
ajst-5302	46	26	to	to	PART
ajst-5302	46	27	form	form	VERB
ajst-5302	46	28	layer	layer	NOUN
ajst-5302	46	29	c1	c1	NOUN
ajst-5302	46	30	.	.	PUNCT
ajst-5302	47	1	the	the	DET
ajst-5302	47	2	feature	feature	NOUN
ajst-5302	47	3	map	map	NOUN
ajst-5302	47	4	in	in	ADP
ajst-5302	47	5	layer	layer	NOUN
ajst-5302	47	6	c1	c1	PROPN
ajst-5302	47	7	will	will	AUX
ajst-5302	47	8	be	be	AUX
ajst-5302	47	9	sampled	sample	VERB
ajst-5302	47	10	twice	twice	ADV
ajst-5302	47	11	to	to	PART
ajst-5302	47	12	reduce	reduce	VERB
ajst-5302	47	13	its	its	PRON
ajst-5302	47	14	size	size	NOUN
ajst-5302	47	15	and	and	CCONJ
ajst-5302	47	16	form	form	NOUN
ajst-5302	47	17	layer	layer	NOUN
ajst-5302	47	18	s1	s1	NOUN
ajst-5302	47	19	.	.	PUNCT
ajst-5302	48	1	typically	typically	ADV
ajst-5302	48	2	,	,	PUNCT
ajst-5302	48	3	the	the	DET
ajst-5302	48	4	pool	pool	NOUN
ajst-5302	48	5	size	size	NOUN
ajst-5302	48	6	during	during	ADP
ajst-5302	48	7	mapping	mapping	NOUN
ajst-5302	48	8	is	be	AUX
ajst-5302	48	9	2×2	2×2	NOUN
ajst-5302	48	10	.	.	PUNCT
ajst-5302	49	1	this	this	DET
ajst-5302	49	2	process	process	NOUN
ajst-5302	49	3	will	will	AUX
ajst-5302	49	4	be	be	AUX
ajst-5302	49	5	repeated	repeat	VERB
ajst-5302	49	6	in	in	ADP
ajst-5302	49	7	layers	layer	NOUN
ajst-5302	49	8	c2	c2	PROPN
ajst-5302	49	9	and	and	CCONJ
ajst-5302	49	10	s2	s2	PROPN
ajst-5302	49	11	.	.	PUNCT
ajst-5302	50	1	after	after	SCONJ
ajst-5302	50	2	sufficient	sufficient	ADJ
ajst-5302	50	3	features	feature	NOUN
ajst-5302	50	4	are	be	AUX
ajst-5302	50	5	extracted	extract	VERB
ajst-5302	50	6	,	,	PUNCT
ajst-5302	50	7	the	the	DET
ajst-5302	50	8	2d	2d	NUM
ajst-5302	50	9	pixel	pixel	PROPN
ajst-5302	50	10	raster	raster	PROPN
ajst-5302	50	11	is	be	AUX
ajst-5302	50	12	converted	convert	VERB
ajst-5302	50	13	into	into	ADP
ajst-5302	50	14	1d	1d	NUM
ajst-5302	50	15	data	datum	NOUN
ajst-5302	50	16	and	and	CCONJ
ajst-5302	50	17	input	input	NOUN
ajst-5302	50	18	into	into	ADP
ajst-5302	50	19	the	the	DET
ajst-5302	50	20	traditional	traditional	ADJ
ajst-5302	50	21	neural	neural	ADJ
ajst-5302	50	22	network	network	NOUN
ajst-5302	50	23	classifier	classifier	NOUN
ajst-5302	50	24	.	.	PUNCT
ajst-5302	51	1	figure	figure	VERB
ajst-5302	51	2	3	3	NUM
ajst-5302	51	3	.	.	PUNCT
ajst-5302	51	4	convolutional	convolutional	ADJ
ajst-5302	51	5	neural	neural	ADJ
ajst-5302	51	6	network	network	NOUN
ajst-5302	51	7	structure	structure	NOUN
ajst-5302	51	8	40	40	NUM
ajst-5302	51	9	entering	enter	VERB
ajst-5302	51	10	the	the	DET
ajst-5302	51	11	convolution	convolution	NOUN
ajst-5302	51	12	layer	layer	NOUN
ajst-5302	51	13	,	,	PUNCT
ajst-5302	51	14	the	the	DET
ajst-5302	51	15	upper	upper	ADJ
ajst-5302	51	16	layer	layer	NOUN
ajst-5302	51	17	feature	feature	NOUN
ajst-5302	51	18	map	map	NOUN
ajst-5302	51	19	is	be	AUX
ajst-5302	51	20	divided	divide	VERB
ajst-5302	51	21	into	into	ADP
ajst-5302	51	22	many	many	ADJ
ajst-5302	51	23	local	local	ADJ
ajst-5302	51	24	regions	region	NOUN
ajst-5302	51	25	and	and	CCONJ
ajst-5302	51	26	convolved	convolve	VERB
ajst-5302	51	27	with	with	ADP
ajst-5302	51	28	trainable	trainable	ADJ
ajst-5302	51	29	kernels	kernel	NOUN
ajst-5302	51	30	respectively	respectively	ADV
ajst-5302	51	31	.	.	PUNCT
ajst-5302	52	1	after	after	ADP
ajst-5302	52	2	processing	processing	NOUN
ajst-5302	52	3	convolution	convolution	NOUN
ajst-5302	52	4	by	by	ADP
ajst-5302	52	5	activation	activation	NOUN
ajst-5302	52	6	function	function	NOUN
ajst-5302	52	7	,	,	PUNCT
ajst-5302	52	8	we	we	PRON
ajst-5302	52	9	will	will	AUX
ajst-5302	52	10	obtain	obtain	VERB
ajst-5302	52	11	new	new	ADJ
ajst-5302	52	12	output	output	NOUN
ajst-5302	52	13	feature	feature	NOUN
ajst-5302	52	14	maps	map	NOUN
ajst-5302	52	15	.	.	PUNCT
ajst-5302	53	1	let	let	VERB
ajst-5302	53	2	the	the	DET
ajst-5302	53	3	𝑙	𝑙	PRON
ajst-5302	53	4	layer	layer	NOUN
ajst-5302	53	5	be	be	AUX
ajst-5302	53	6	the	the	DET
ajst-5302	53	7	convolutional	convolutional	ADJ
ajst-5302	53	8	layer	layer	NOUN
ajst-5302	53	9	,	,	PUNCT
ajst-5302	53	10	and	and	CCONJ
ajst-5302	53	11	the	the	DET
ajst-5302	53	12	output	output	NOUN
ajst-5302	53	13	of	of	ADP
ajst-5302	53	14	𝑗	𝑗	PRON
ajst-5302	53	15	layer	layer	NOUN
ajst-5302	53	16	can	can	AUX
ajst-5302	53	17	be	be	AUX
ajst-5302	53	18	expressed	express	VERB
ajst-5302	53	19	as	as	ADP
ajst-5302	53	20	:	:	PUNCT
ajst-5302	53	21	𝑋	𝑋	PROPN
ajst-5302	53	22	𝑓	𝑓	PROPN
ajst-5302	53	23	∑	∑	PUNCT
ajst-5302	53	24	𝑋	𝑋	PROPN
ajst-5302	53	25	∗	∗	NOUN
ajst-5302	53	26	𝐾	𝐾	PROPN
ajst-5302	53	27	𝑏∈	𝑏∈	PROPN
ajst-5302	53	28	(	(	PUNCT
ajst-5302	53	29	3	3	NUM
ajst-5302	53	30	)	)	PUNCT
ajst-5302	53	31	in	in	ADP
ajst-5302	53	32	equation	equation	NOUN
ajst-5302	53	33	(	(	PUNCT
ajst-5302	53	34	3),𝑀	3),𝑀	NUM
ajst-5302	53	35	represents	represent	VERB
ajst-5302	53	36	the	the	DET
ajst-5302	53	37	local	local	ADJ
ajst-5302	53	38	region	region	NOUN
ajst-5302	53	39	connected	connect	VERB
ajst-5302	53	40	by	by	ADP
ajst-5302	53	41	the	the	DET
ajst-5302	53	42	𝑗	𝑗	PROPN
ajst-5302	53	43	kernel,𝐾	kernel,𝐾	NOUN
ajst-5302	53	44	is	be	AUX
ajst-5302	53	45	a	a	DET
ajst-5302	53	46	parameter	parameter	NOUN
ajst-5302	53	47	of	of	ADP
ajst-5302	53	48	the	the	DET
ajst-5302	53	49	convolution	convolution	NOUN
ajst-5302	53	50	kernel	kernel	NOUN
ajst-5302	53	51	,	,	PUNCT
ajst-5302	53	52	𝐾	𝐾	PROPN
ajst-5302	53	53	is	be	AUX
ajst-5302	53	54	a	a	DET
ajst-5302	53	55	bias	bias	NOUN
ajst-5302	53	56	,	,	PUNCT
ajst-5302	53	57	𝑓	𝑓	PRON
ajst-5302	53	58	is	be	AUX
ajst-5302	53	59	a	a	DET
ajst-5302	53	60	gaussian	gaussian	ADJ
ajst-5302	53	61	kernel	kernel	NOUN
ajst-5302	53	62	.	.	PUNCT
ajst-5302	54	1	in	in	ADP
ajst-5302	54	2	the	the	DET
ajst-5302	54	3	subsampling	subsample	VERB
ajst-5302	54	4	layer	layer	NOUN
ajst-5302	54	5	,	,	PUNCT
ajst-5302	54	6	the	the	DET
ajst-5302	54	7	most	most	ADV
ajst-5302	54	8	common	common	ADJ
ajst-5302	54	9	method	method	NOUN
ajst-5302	54	10	is	be	AUX
ajst-5302	54	11	the	the	DET
ajst-5302	54	12	mean	mean	ADJ
ajst-5302	54	13	pool	pool	NOUN
ajst-5302	54	14	in	in	ADP
ajst-5302	54	15	the	the	DET
ajst-5302	54	16	2×2	2×2	NUM
ajst-5302	54	17	region	region	NOUN
ajst-5302	54	18	.	.	PUNCT
ajst-5302	55	1	that	that	PRON
ajst-5302	55	2	is	be	AUX
ajst-5302	55	3	,	,	PUNCT
ajst-5302	55	4	an	an	DET
ajst-5302	55	5	average	average	NOUN
ajst-5302	55	6	of	of	ADP
ajst-5302	55	7	4	4	NUM
ajst-5302	55	8	points	point	NOUN
ajst-5302	55	9	in	in	ADP
ajst-5302	55	10	the	the	DET
ajst-5302	55	11	area	area	NOUN
ajst-5302	55	12	serve	serve	VERB
ajst-5302	55	13	as	as	ADP
ajst-5302	55	14	new	new	ADJ
ajst-5302	55	15	pixel	pixel	PROPN
ajst-5302	55	16	values	value	NOUN
ajst-5302	55	17	.	.	PUNCT
ajst-5302	56	1	parameter	parameter	PROPN
ajst-5302	56	2	estimation	estimation	NOUN
ajst-5302	56	3	in	in	ADP
ajst-5302	56	4	cnn	cnn	PROPN
ajst-5302	56	5	still	still	ADV
ajst-5302	56	6	uses	use	VERB
ajst-5302	56	7	the	the	DET
ajst-5302	56	8	gradient	gradient	ADJ
ajst-5302	56	9	algorithm	algorithm	NOUN
ajst-5302	56	10	of	of	ADP
ajst-5302	56	11	back	back	ADJ
ajst-5302	56	12	propagation	propagation	NOUN
ajst-5302	56	13	.	.	PUNCT
ajst-5302	57	1	however	however	ADV
ajst-5302	57	2	,	,	PUNCT
ajst-5302	57	3	according	accord	VERB
ajst-5302	57	4	to	to	ADP
ajst-5302	57	5	the	the	DET
ajst-5302	57	6	characteristics	characteristic	NOUN
ajst-5302	57	7	of	of	ADP
ajst-5302	57	8	cnn	cnn	PROPN
ajst-5302	57	9	,	,	PUNCT
ajst-5302	57	10	we	we	PRON
ajst-5302	57	11	should	should	AUX
ajst-5302	57	12	make	make	VERB
ajst-5302	57	13	some	some	DET
ajst-5302	57	14	modifications	modification	NOUN
ajst-5302	57	15	in	in	ADP
ajst-5302	57	16	a	a	DET
ajst-5302	57	17	few	few	ADJ
ajst-5302	57	18	specific	specific	ADJ
ajst-5302	57	19	steps	step	NOUN
ajst-5302	57	20	.	.	PUNCT
ajst-5302	58	1	here	here	ADV
ajst-5302	58	2	,	,	PUNCT
ajst-5302	58	3	assume	assume	VERB
ajst-5302	58	4	that	that	SCONJ
ajst-5302	58	5	the	the	DET
ajst-5302	58	6	residual	residual	ADJ
ajst-5302	58	7	error	error	NOUN
ajst-5302	58	8	vector	vector	NOUN
ajst-5302	58	9	propagated	propagate	VERB
ajst-5302	58	10	to	to	ADP
ajst-5302	58	11	the	the	DET
ajst-5302	58	12	raster	raster	NOUN
ajst-5302	58	13	layer	layer	NOUN
ajst-5302	58	14	is	be	AUX
ajst-5302	58	15	.	.	PUNCT
ajst-5302	59	1	specifically	specifically	ADV
ajst-5302	59	2	,	,	PUNCT
ajst-5302	59	3	it	it	PRON
ajst-5302	59	4	can	can	AUX
ajst-5302	59	5	be	be	AUX
ajst-5302	59	6	expressed	express	VERB
ajst-5302	59	7	as	as	ADP
ajst-5302	59	8	formula	formula	NOUN
ajst-5302	59	9	(	(	PUNCT
ajst-5302	59	10	4	4	NUM
ajst-5302	59	11	)	)	PUNCT
ajst-5302	59	12	.	.	PUNCT
ajst-5302	60	1	𝑑	𝑑	PROPN
ajst-5302	60	2	𝑑	𝑑	PROPN
ajst-5302	60	3	,	,	PUNCT
ajst-5302	60	4	𝑑	𝑑	PRON
ajst-5302	60	5	,	,	PUNCT
ajst-5302	60	6	…	…	PUNCT
ajst-5302	60	7	,	,	PUNCT
ajst-5302	60	8	𝑑	𝑑	X
ajst-5302	60	9	(	(	PUNCT
ajst-5302	60	10	4	4	NUM
ajst-5302	60	11	)	)	PUNCT
ajst-5302	60	12	since	since	SCONJ
ajst-5302	60	13	rasterization	rasterization	NOUN
ajst-5302	60	14	is	be	AUX
ajst-5302	60	15	a	a	DET
ajst-5302	60	16	2d	2d	NUM
ajst-5302	60	17	-	-	PUNCT
ajst-5302	60	18	to-1d	to-1d	NOUN
ajst-5302	60	19	transmission	transmission	NOUN
ajst-5302	60	20	,	,	PUNCT
ajst-5302	60	21	it	it	PRON
ajst-5302	60	22	is	be	AUX
ajst-5302	60	23	only	only	ADV
ajst-5302	60	24	necessary	necessary	ADJ
ajst-5302	60	25	to	to	PART
ajst-5302	60	26	reorganize	reorganize	VERB
ajst-5302	60	27	the	the	DET
ajst-5302	60	28	residual	residual	ADJ
ajst-5302	60	29	error	error	NOUN
ajst-5302	60	30	vector	vector	NOUN
ajst-5302	60	31	from	from	ADP
ajst-5302	60	32	the	the	DET
ajst-5302	60	33	1d	1d	NUM
ajst-5302	60	34	to	to	ADP
ajst-5302	60	35	2d	2d	NUM
ajst-5302	60	36	matrix	matrix	NOUN
ajst-5302	60	37	and	and	CCONJ
ajst-5302	60	38	pass	pass	VERB
ajst-5302	60	39	it	it	PRON
ajst-5302	60	40	back	back	ADV
ajst-5302	60	41	to	to	ADP
ajst-5302	60	42	the	the	DET
ajst-5302	60	43	subsampling	subsample	VERB
ajst-5302	60	44	layer	layer	NOUN
ajst-5302	60	45	.	.	PUNCT
ajst-5302	61	1	when	when	SCONJ
ajst-5302	61	2	inverting	invert	VERB
ajst-5302	61	3	from	from	ADP
ajst-5302	61	4	s	s	PRON
ajst-5302	61	5	layer	layer	NOUN
ajst-5302	61	6	to	to	ADP
ajst-5302	61	7	c	c	PROPN
ajst-5302	61	8	layer	layer	NOUN
ajst-5302	61	9	,	,	PUNCT
ajst-5302	61	10	different	different	ADJ
ajst-5302	61	11	pooling	pooling	NOUN
ajst-5302	61	12	methods	method	NOUN
ajst-5302	61	13	correspond	correspond	VERB
ajst-5302	61	14	to	to	ADP
ajst-5302	61	15	different	different	ADJ
ajst-5302	61	16	processes	process	NOUN
ajst-5302	61	17	of	of	ADP
ajst-5302	61	18	back	back	ADJ
ajst-5302	61	19	propagation	propagation	NOUN
ajst-5302	61	20	of	of	ADP
ajst-5302	61	21	residual	residual	ADJ
ajst-5302	61	22	error	error	NOUN
ajst-5302	61	23	.	.	PUNCT
ajst-5302	62	1	in	in	ADP
ajst-5302	62	2	the	the	DET
ajst-5302	62	3	average	average	ADJ
ajst-5302	62	4	convergence	convergence	NOUN
ajst-5302	62	5	,	,	PUNCT
ajst-5302	62	6	we	we	PRON
ajst-5302	62	7	simply	simply	ADV
ajst-5302	62	8	average	average	VERB
ajst-5302	62	9	the	the	DET
ajst-5302	62	10	residual	residual	ADJ
ajst-5302	62	11	error	error	NOUN
ajst-5302	62	12	of	of	ADP
ajst-5302	62	13	the	the	DET
ajst-5302	62	14	current	current	ADJ
ajst-5302	62	15	point	point	NOUN
ajst-5302	62	16	to	to	ADP
ajst-5302	62	17	the	the	DET
ajst-5302	62	18	top	top	ADJ
ajst-5302	62	19	4	4	NUM
ajst-5302	62	20	points	point	NOUN
ajst-5302	62	21	.	.	PUNCT
ajst-5302	63	1	here	here	ADV
ajst-5302	63	2	,	,	PUNCT
ajst-5302	63	3	it	it	PRON
ajst-5302	63	4	is	be	AUX
ajst-5302	63	5	recommended	recommend	VERB
ajst-5302	63	6	that	that	SCONJ
ajst-5302	63	7	the	the	DET
ajst-5302	63	8	residuals	residual	NOUN
ajst-5302	63	9	at	at	ADP
ajst-5302	63	10	the	the	DET
ajst-5302	63	11	point	point	NOUN
ajst-5302	63	12	of	of	ADP
ajst-5302	63	13	layer	layer	NOUN
ajst-5302	63	14	s	s	AUX
ajst-5302	63	15	be	be	AUX
ajst-5302	63	16	𝛥	𝛥	PROPN
ajst-5302	63	17	.	.	PUNCT
ajst-5302	64	1	after	after	ADP
ajst-5302	64	2	subsampling	subsample	VERB
ajst-5302	64	3	,	,	PUNCT
ajst-5302	64	4	the	the	DET
ajst-5302	64	5	error	error	NOUN
ajst-5302	64	6	transmitted	transmit	VERB
ajst-5302	64	7	to	to	ADP
ajst-5302	64	8	layer	layer	NOUN
ajst-5302	64	9	c	c	PROPN
ajst-5302	64	10	can	can	AUX
ajst-5302	64	11	be	be	AUX
ajst-5302	64	12	expressed	express	VERB
ajst-5302	64	13	as	as	ADP
ajst-5302	64	14	equation	equation	NOUN
ajst-5302	64	15	(	(	PUNCT
ajst-5302	64	16	5	5	NUM
ajst-5302	64	17	)	)	PUNCT
ajst-5302	64	18	.	.	PUNCT
ajst-5302	65	1	𝛥	𝛥	PRON
ajst-5302	65	2	𝑢𝑝𝑠𝑎𝑚𝑝𝑙𝑒	𝑢𝑝𝑠𝑎𝑚𝑝𝑙𝑒	ADJ
ajst-5302	65	3	𝛥	𝛥	PROPN
ajst-5302	65	4	(	(	PUNCT
ajst-5302	65	5	5	5	NUM
ajst-5302	65	6	)	)	PUNCT
ajst-5302	65	7	next	next	ADV
ajst-5302	65	8	,	,	PUNCT
ajst-5302	65	9	the	the	DET
ajst-5302	65	10	trainable	trainable	ADJ
ajst-5302	65	11	parameters	parameter	NOUN
ajst-5302	65	12	in	in	ADP
ajst-5302	65	13	layer	layer	NOUN
ajst-5302	65	14	c	c	NOUN
ajst-5302	65	15	are	be	AUX
ajst-5302	65	16	analyzed	analyze	VERB
ajst-5302	65	17	.	.	PUNCT
ajst-5302	66	1	layer	layer	NOUN
ajst-5302	66	2	c	c	PROPN
ajst-5302	66	3	has	have	VERB
ajst-5302	66	4	two	two	NUM
ajst-5302	66	5	tasks	task	NOUN
ajst-5302	66	6	in	in	ADP
ajst-5302	66	7	backpropagation	backpropagation	NOUN
ajst-5302	66	8	:	:	PUNCT
ajst-5302	66	9	reverse	reverse	VERB
ajst-5302	66	10	the	the	DET
ajst-5302	66	11	residuals	residual	NOUN
ajst-5302	66	12	and	and	CCONJ
ajst-5302	66	13	update	update	VERB
ajst-5302	66	14	their	their	PRON
ajst-5302	66	15	parameters	parameter	NOUN
ajst-5302	66	16	.	.	PUNCT
ajst-5302	67	1	according	accord	VERB
ajst-5302	67	2	to	to	ADP
ajst-5302	67	3	bp	bp	PROPN
ajst-5302	67	4	algorithm	algorithm	PROPN
ajst-5302	67	5	and	and	CCONJ
ajst-5302	67	6	considering	consider	VERB
ajst-5302	67	7	convolution	convolution	NOUN
ajst-5302	67	8	operation	operation	NOUN
ajst-5302	67	9	,	,	PUNCT
ajst-5302	67	10	we	we	PRON
ajst-5302	67	11	can	can	AUX
ajst-5302	67	12	get	get	VERB
ajst-5302	67	13	the	the	DET
ajst-5302	67	14	formula	formula	NOUN
ajst-5302	67	15	for	for	ADP
ajst-5302	67	16	updating	update	VERB
ajst-5302	67	17	parameter	parameter	NOUN
ajst-5302	67	18	𝜃	𝜃	NOUN
ajst-5302	67	19	in	in	ADP
ajst-5302	67	20	the	the	DET
ajst-5302	67	21	convolution	convolution	NOUN
ajst-5302	67	22	layer	layer	NOUN
ajst-5302	67	23	as	as	SCONJ
ajst-5302	67	24	follows	follow	VERB
ajst-5302	67	25	:	:	PUNCT
ajst-5302	68	1	𝑟𝑜𝑡180	𝑟𝑜𝑡180	PROPN
ajst-5302	68	2	∑𝑋	∑𝑋	PROPN
ajst-5302	68	3	∗	∗	VERB
ajst-5302	68	4	𝑟𝑜𝑡180	𝑟𝑜𝑡180	PROPN
ajst-5302	68	5	𝛥𝑝	𝛥𝑝	PROPN
ajst-5302	68	6	(	(	PUNCT
ajst-5302	68	7	6	6	NUM
ajst-5302	68	8	)	)	PUNCT
ajst-5302	68	9	in	in	ADP
ajst-5302	68	10	equation，𝑟𝑜𝑡180	equation，𝑟𝑜𝑡180	PROPN
ajst-5302	68	11	represents	represent	VERB
ajst-5302	68	12	a	a	DET
ajst-5302	68	13	180	180	NUM
ajst-5302	68	14	degree	degree	NOUN
ajst-5302	68	15	rotation	rotation	NOUN
ajst-5302	68	16	of	of	ADP
ajst-5302	68	17	the	the	DET
ajst-5302	68	18	matrix	matrix	NOUN
ajst-5302	68	19	.	.	PUNCT
ajst-5302	69	1	if	if	SCONJ
ajst-5302	69	2	the	the	DET
ajst-5302	69	3	feature	feature	NOUN
ajst-5302	69	4	mapping	mapping	NOUN
ajst-5302	69	5	in	in	ADP
ajst-5302	69	6	the	the	DET
ajst-5302	69	7	former	former	ADJ
ajst-5302	69	8	s	s	PART
ajst-5302	69	9	layer	layer	NOUN
ajst-5302	69	10	𝑞′	𝑞′	NOUN
ajst-5302	69	11	is	be	AUX
ajst-5302	69	12	connected	connect	VERB
ajst-5302	69	13	to	to	ADP
ajst-5302	69	14	the	the	DET
ajst-5302	69	15	set	set	NOUN
ajst-5302	69	16	c	c	PROPN
ajst-5302	69	17	in	in	ADP
ajst-5302	69	18	the	the	DET
ajst-5302	69	19	convolution	convolution	NOUN
ajst-5302	69	20	layer	layer	NOUN
ajst-5302	69	21	𝑝	𝑝	PROPN
ajst-5302	69	22	,	,	PUNCT
ajst-5302	69	23	the	the	DET
ajst-5302	69	24	extended	extended	ADJ
ajst-5302	69	25	residual	residual	ADJ
ajst-5302	69	26	error	error	NOUN
ajst-5302	69	27	𝑞′	𝑞′	NOUN
ajst-5302	69	28	can	can	AUX
ajst-5302	69	29	be	be	AUX
ajst-5302	69	30	expressed	express	VERB
ajst-5302	69	31	as	as	ADP
ajst-5302	69	32	:	:	PUNCT
ajst-5302	69	33	𝛥	𝛥	NOUN
ajst-5302	69	34	′	′	NOUN
ajst-5302	69	35	∑	∑	PUNCT
ajst-5302	69	36	𝛥	𝛥	PROPN
ajst-5302	69	37	∗	∗	NOUN
ajst-5302	69	38	𝑟𝑜𝑡180	𝑟𝑜𝑡180	PROPN
ajst-5302	69	39	𝛥𝑝∈	𝛥𝑝∈	PROPN
ajst-5302	69	40	𝑋	𝑋	PROPN
ajst-5302	69	41	′	′	X
ajst-5302	69	42	(	(	PUNCT
ajst-5302	69	43	7	7	NUM
ajst-5302	69	44	)	)	PUNCT
ajst-5302	69	45	after	after	ADP
ajst-5302	69	46	updating	update	VERB
ajst-5302	69	47	all	all	DET
ajst-5302	69	48	parameters	parameter	NOUN
ajst-5302	69	49	,	,	PUNCT
ajst-5302	69	50	the	the	DET
ajst-5302	69	51	network	network	NOUN
ajst-5302	69	52	completes	complete	VERB
ajst-5302	69	53	a	a	DET
ajst-5302	69	54	round	round	NOUN
ajst-5302	69	55	of	of	ADP
ajst-5302	69	56	training	training	NOUN
ajst-5302	69	57	,	,	PUNCT
ajst-5302	69	58	which	which	PRON
ajst-5302	69	59	should	should	AUX
ajst-5302	69	60	be	be	AUX
ajst-5302	69	61	performed	perform	VERB
ajst-5302	69	62	for	for	ADP
ajst-5302	69	63	all	all	DET
ajst-5302	69	64	training	training	NOUN
ajst-5302	69	65	samples	sample	NOUN
ajst-5302	69	66	until	until	SCONJ
ajst-5302	69	67	the	the	DET
ajst-5302	69	68	entire	entire	ADJ
ajst-5302	69	69	network	network	NOUN
ajst-5302	69	70	meets	meet	VERB
ajst-5302	69	71	the	the	DET
ajst-5302	69	72	training	training	NOUN
ajst-5302	69	73	requirements	requirement	NOUN
ajst-5302	69	74	.	.	PUNCT
ajst-5302	70	1	2.3	2.3	NUM
ajst-5302	70	2	.	.	PUNCT
ajst-5302	70	3	principle	principle	NOUN
ajst-5302	70	4	of	of	ADP
ajst-5302	70	5	ssd	ssd	ADJ
ajst-5302	70	6	algorithm	algorithm	NOUN
ajst-5302	70	7	the	the	DET
ajst-5302	70	8	ssd	ssd	NOUN
ajst-5302	70	9	algorithm	algorithm	NOUN
ajst-5302	70	10	is	be	AUX
ajst-5302	70	11	a	a	DET
ajst-5302	70	12	branch	branch	NOUN
ajst-5302	70	13	algorithm	algorithm	NOUN
ajst-5302	70	14	of	of	ADP
ajst-5302	70	15	cnn	cnn	PROPN
ajst-5302	70	16	.	.	PUNCT
ajst-5302	71	1	ssd	ssd	PROPN
ajst-5302	71	2	convolutional	convolutional	ADJ
ajst-5302	71	3	neural	neural	ADJ
ajst-5302	71	4	network	network	NOUN
ajst-5302	71	5	is	be	AUX
ajst-5302	71	6	a	a	DET
ajst-5302	71	7	supervised	supervised	ADJ
ajst-5302	71	8	deep	deep	ADJ
ajst-5302	71	9	learning	learning	NOUN
ajst-5302	71	10	model	model	NOUN
ajst-5302	71	11	,	,	PUNCT
ajst-5302	71	12	which	which	PRON
ajst-5302	71	13	is	be	AUX
ajst-5302	71	14	mainly	mainly	ADV
ajst-5302	71	15	composed	compose	VERB
ajst-5302	71	16	of	of	ADP
ajst-5302	71	17	three	three	NUM
ajst-5302	71	18	layers	layer	NOUN
ajst-5302	71	19	:	:	PUNCT
ajst-5302	71	20	convolutional	convolutional	ADJ
ajst-5302	71	21	layer	layer	NOUN
ajst-5302	71	22	,	,	PUNCT
ajst-5302	71	23	activation	activation	NOUN
ajst-5302	71	24	function	function	NOUN
ajst-5302	71	25	layer	layer	NOUN
ajst-5302	71	26	and	and	CCONJ
ajst-5302	71	27	pooling	pool	VERB
ajst-5302	71	28	layer	layer	NOUN
ajst-5302	71	29	.	.	PUNCT
ajst-5302	72	1	in	in	ADP
ajst-5302	72	2	a	a	DET
ajst-5302	72	3	standard	standard	ADJ
ajst-5302	72	4	ssd	ssd	NOUN
ajst-5302	72	5	(	(	PUNCT
ajst-5302	72	6	ssd300	ssd300	PROPN
ajst-5302	72	7	)	)	PUNCT
ajst-5302	72	8	,	,	PUNCT
ajst-5302	72	9	8732	8732	NUM
ajst-5302	72	10	bounding	bounding	NOUN
ajst-5302	72	11	boxes	box	NOUN
ajst-5302	72	12	and	and	CCONJ
ajst-5302	72	13	8732	8732	NUM
ajst-5302	72	14	scores	score	NOUN
ajst-5302	72	15	per	per	ADP
ajst-5302	72	16	category	category	NOUN
ajst-5302	72	17	are	be	AUX
ajst-5302	72	18	generated	generate	VERB
ajst-5302	72	19	for	for	ADP
ajst-5302	72	20	each	each	DET
ajst-5302	72	21	input	input	NOUN
ajst-5302	72	22	image	image	NOUN
ajst-5302	72	23	.	.	PUNCT
ajst-5302	73	1	the	the	DET
ajst-5302	73	2	output	output	NOUN
ajst-5302	73	3	after	after	ADP
ajst-5302	73	4	the	the	DET
ajst-5302	73	5	non	non	ADJ
ajst-5302	73	6	-	-	ADJ
ajst-5302	73	7	maximum	maximum	ADJ
ajst-5302	73	8	suppression	suppression	NOUN
ajst-5302	73	9	step	step	NOUN
ajst-5302	73	10	is	be	AUX
ajst-5302	73	11	the	the	DET
ajst-5302	73	12	final	final	ADJ
ajst-5302	73	13	detection	detection	NOUN
ajst-5302	73	14	result	result	NOUN
ajst-5302	73	15	.	.	PUNCT
ajst-5302	74	1	non	non	ADJ
ajst-5302	74	2	-	-	ADJ
ajst-5302	74	3	maximum	maximum	ADJ
ajst-5302	74	4	suppression	suppression	NOUN
ajst-5302	74	5	is	be	AUX
ajst-5302	74	6	an	an	DET
ajst-5302	74	7	algorithm	algorithm	NOUN
ajst-5302	74	8	that	that	PRON
ajst-5302	74	9	attempts	attempt	VERB
ajst-5302	74	10	to	to	PART
ajst-5302	74	11	eliminate	eliminate	VERB
ajst-5302	74	12	additional	additional	ADJ
ajst-5302	74	13	boundary	boundary	ADJ
ajst-5302	74	14	boxes	box	NOUN
ajst-5302	74	15	based	base	VERB
ajst-5302	74	16	on	on	ADP
ajst-5302	74	17	scores	score	NOUN
ajst-5302	74	18	and	and	CCONJ
ajst-5302	74	19	intersections	intersection	NOUN
ajst-5302	74	20	.	.	PUNCT
ajst-5302	75	1	in	in	ADP
ajst-5302	75	2	other	other	ADJ
ajst-5302	75	3	words	word	NOUN
ajst-5302	75	4	,	,	PUNCT
ajst-5302	75	5	nonmaximum	nonmaximum	ADJ
ajst-5302	75	6	suppression	suppression	NOUN
ajst-5302	75	7	attempts	attempt	VERB
ajst-5302	75	8	to	to	PART
ajst-5302	75	9	merge	merge	VERB
ajst-5302	75	10	all	all	DET
ajst-5302	75	11	bounds	bound	NOUN
ajst-5302	75	12	suggested	suggest	VERB
ajst-5302	75	13	as	as	ADP
ajst-5302	75	14	unique	unique	ADJ
ajst-5302	75	15	objects	object	NOUN
ajst-5302	75	16	.	.	PUNCT
ajst-5302	76	1	in	in	ADP
ajst-5302	76	2	the	the	DET
ajst-5302	76	3	early	early	ADJ
ajst-5302	76	4	layers	layer	NOUN
ajst-5302	76	5	of	of	ADP
ajst-5302	76	6	ssds	ssds	NOUN
ajst-5302	76	7	,	,	PUNCT
ajst-5302	76	8	a	a	DET
ajst-5302	76	9	standard	standard	ADJ
ajst-5302	76	10	architecture	architecture	NOUN
ajst-5302	76	11	called	call	VERB
ajst-5302	76	12	the	the	DET
ajst-5302	76	13	underlying	underlie	VERB
ajst-5302	76	14	network	network	NOUN
ajst-5302	76	15	was	be	AUX
ajst-5302	76	16	used	use	VERB
ajst-5302	76	17	for	for	ADP
ajst-5302	76	18	high	high	ADJ
ajst-5302	76	19	-	-	PUNCT
ajst-5302	76	20	quality	quality	NOUN
ajst-5302	76	21	image	image	NOUN
ajst-5302	76	22	representation	representation	NOUN
ajst-5302	76	23	(	(	PUNCT
ajst-5302	76	24	truncated	truncate	VERB
ajst-5302	76	25	before	before	ADP
ajst-5302	76	26	any	any	DET
ajst-5302	76	27	classification	classification	NOUN
ajst-5302	76	28	layer	layer	NOUN
ajst-5302	76	29	)	)	PUNCT
ajst-5302	76	30	.	.	PUNCT
ajst-5302	77	1	3	3	X
ajst-5302	77	2	.	.	X
ajst-5302	77	3	vehicle	vehicle	NOUN
ajst-5302	77	4	object	object	NOUN
ajst-5302	77	5	detection	detection	NOUN
ajst-5302	77	6	based	base	VERB
ajst-5302	77	7	on	on	ADP
ajst-5302	77	8	ssd	ssd	PROPN
ajst-5302	77	9	3.1	3.1	NUM
ajst-5302	77	10	.	.	PUNCT
ajst-5302	77	11	object	object	NOUN
ajst-5302	77	12	detection	detection	NOUN
ajst-5302	77	13	analysis	analysis	NOUN
ajst-5302	77	14	based	base	VERB
ajst-5302	77	15	on	on	ADP
ajst-5302	77	16	opencv	opencv	PROPN
ajst-5302	77	17	this	this	DET
ajst-5302	77	18	chapter	chapter	NOUN
ajst-5302	77	19	combines	combine	VERB
ajst-5302	77	20	mobilenet	mobilenet	PROPN
ajst-5302	77	21	ssd	ssd	PROPN
ajst-5302	77	22	and	and	CCONJ
ajst-5302	77	23	dnn	dnn	PROPN
ajst-5302	77	24	module	module	NOUN
ajst-5302	77	25	in	in	ADP
ajst-5302	77	26	opencv	opencv	PROPN
ajst-5302	77	27	to	to	PART
ajst-5302	77	28	build	build	VERB
ajst-5302	77	29	deep	deep	ADJ
ajst-5302	77	30	learning	learning	NOUN
ajst-5302	77	31	object	object	NOUN
ajst-5302	77	32	detector	detector	NOUN
ajst-5302	77	33	.	.	PUNCT
ajst-5302	78	1	this	this	DET
ajst-5302	78	2	article	article	NOUN
ajst-5302	78	3	introduces	introduce	VERB
ajst-5302	78	4	the	the	DET
ajst-5302	78	5	code	code	NOUN
ajst-5302	78	6	in	in	ADP
ajst-5302	78	7	several	several	ADJ
ajst-5302	78	8	parts	part	NOUN
ajst-5302	78	9	.	.	PUNCT
ajst-5302	79	1	first	first	ADV
ajst-5302	79	2	,	,	PUNCT
ajst-5302	79	3	create	create	VERB
ajst-5302	79	4	anew	anew	ADJ
ajst-5302	79	5	project	project	NOUN
ajst-5302	79	6	file	file	NOUN
ajst-5302	79	7	in	in	ADP
ajst-5302	79	8	python	python	NOUN
ajst-5302	79	9	and	and	CCONJ
ajst-5302	79	10	write	write	VERB
ajst-5302	79	11	code	code	NOUN
ajst-5302	79	12	to	to	PART
ajst-5302	79	13	import	import	VERB
ajst-5302	79	14	some	some	PRON
ajst-5302	79	15	of	of	ADP
ajst-5302	79	16	the	the	DET
ajst-5302	79	17	necessary	necessary	ADJ
ajst-5302	79	18	toolkits	toolkit	NOUN
ajst-5302	79	19	in	in	ADP
ajst-5302	79	20	opencv	opencv	PROPN
ajst-5302	79	21	,	,	PUNCT
ajst-5302	79	22	as	as	SCONJ
ajst-5302	79	23	shown	show	VERB
ajst-5302	79	24	in	in	ADP
ajst-5302	79	25	fig	fig	NOUN
ajst-5302	79	26	.	.	PUNCT
ajst-5302	80	1	4	4	X
ajst-5302	80	2	.	.	X
ajst-5302	80	3	figure	figure	VERB
ajst-5302	80	4	4	4	NUM
ajst-5302	80	5	.	.	NOUN
ajst-5302	80	6	importing	import	VERB
ajst-5302	80	7	and	and	CCONJ
ajst-5302	80	8	setting	set	VERB
ajst-5302	80	9	the	the	DET
ajst-5302	80	10	tool	tool	NOUN
ajst-5302	80	11	package	package	NOUN
ajst-5302	80	12	set	set	VERB
ajst-5302	80	13	the	the	DET
ajst-5302	80	14	necessary	necessary	ADJ
ajst-5302	80	15	settings	setting	NOUN
ajst-5302	80	16	for	for	ADP
ajst-5302	80	17	target	target	NOUN
ajst-5302	80	18	detection	detection	NOUN
ajst-5302	80	19	.	.	PUNCT
ajst-5302	81	1	the	the	DET
ajst-5302	81	2	setting	set	VERB
ajst-5302	81	3	methods	method	NOUN
ajst-5302	81	4	are	be	AUX
ajst-5302	81	5	manual	manual	ADJ
ajst-5302	81	6	and	and	CCONJ
ajst-5302	81	7	automatic	automatic	ADJ
ajst-5302	81	8	.	.	PUNCT
ajst-5302	82	1	--image	--image	PUNCT
ajst-5302	82	2	indicates	indicate	VERB
ajst-5302	82	3	setting	set	VERB
ajst-5302	82	4	41	41	NUM
ajst-5302	82	5	the	the	DET
ajst-5302	82	6	path	path	NOUN
ajst-5302	82	7	to	to	ADP
ajst-5302	82	8	the	the	DET
ajst-5302	82	9	input	input	NOUN
ajst-5302	82	10	image	image	NOUN
ajst-5302	82	11	,	,	PUNCT
ajst-5302	82	12	-prototxt	-prototxt	PROPN
ajst-5302	82	13	indicates	indicate	VERB
ajst-5302	82	14	setting	set	VERB
ajst-5302	82	15	the	the	DET
ajst-5302	82	16	location	location	NOUN
ajst-5302	82	17	of	of	ADP
ajst-5302	82	18	the	the	DET
ajst-5302	82	19	caffe	caffe	NOUN
ajst-5302	82	20	file	file	NOUN
ajst-5302	82	21	,	,	PUNCT
ajst-5302	82	22	--model	--model	PUNCT
ajst-5302	82	23	indicates	indicate	VERB
ajst-5302	82	24	setting	set	VERB
ajst-5302	82	25	the	the	DET
ajst-5302	82	26	location	location	NOUN
ajst-5302	82	27	of	of	ADP
ajst-5302	82	28	the	the	DET
ajst-5302	82	29	pre	pre	ADJ
ajst-5302	82	30	-	-	ADJ
ajst-5302	82	31	training	training	ADJ
ajst-5302	82	32	model	model	NOUN
ajst-5302	82	33	,	,	PUNCT
ajst-5302	82	34	and	and	CCONJ
ajst-5302	82	35	--confidence	--confidence	NUM
ajst-5302	82	36	indicates	indicate	VERB
ajst-5302	82	37	setting	set	VERB
ajst-5302	82	38	the	the	DET
ajst-5302	82	39	minimum	minimum	ADJ
ajst-5302	82	40	probability	probability	NOUN
ajst-5302	82	41	threshold	threshold	NOUN
ajst-5302	82	42	of	of	ADP
ajst-5302	82	43	the	the	DET
ajst-5302	82	44	filter	filter	NOUN
ajst-5302	82	45	weak	weak	ADJ
ajst-5302	82	46	signal	signal	NOUN
ajst-5302	82	47	detector	detector	NOUN
ajst-5302	82	48	,	,	PUNCT
ajst-5302	82	49	the	the	DET
ajst-5302	82	50	default	default	NOUN
ajst-5302	82	51	value	value	NOUN
ajst-5302	82	52	being	be	AUX
ajst-5302	82	53	20	20	NUM
ajst-5302	82	54	%	%	NOUN
ajst-5302	82	55	.	.	PUNCT
ajst-5302	83	1	after	after	ADP
ajst-5302	83	2	completing	complete	VERB
ajst-5302	83	3	this	this	DET
ajst-5302	83	4	series	series	NOUN
ajst-5302	83	5	of	of	ADP
ajst-5302	83	6	preparatory	preparatory	ADJ
ajst-5302	83	7	work	work	NOUN
ajst-5302	83	8	,	,	PUNCT
ajst-5302	83	9	set	set	VERB
ajst-5302	83	10	class	class	NOUN
ajst-5302	83	11	labels	label	NOUN
ajst-5302	83	12	and	and	CCONJ
ajst-5302	83	13	detection	detection	NOUN
ajst-5302	83	14	box	box	NOUN
ajst-5302	83	15	colors	color	NOUN
ajst-5302	83	16	for	for	ADP
ajst-5302	83	17	detection	detection	NOUN
ajst-5302	83	18	targets	target	NOUN
ajst-5302	83	19	,	,	PUNCT
ajst-5302	83	20	as	as	SCONJ
ajst-5302	83	21	shown	show	VERB
ajst-5302	83	22	in	in	ADP
ajst-5302	83	23	fig	fig	NOUN
ajst-5302	83	24	.	.	PUNCT
ajst-5302	84	1	5	5	X
ajst-5302	84	2	.	.	X
ajst-5302	84	3	figure	figure	NOUN
ajst-5302	84	4	5	5	NUM
ajst-5302	84	5	.	.	PUNCT
ajst-5302	85	1	setting	set	VERB
ajst-5302	85	2	labels	label	NOUN
ajst-5302	85	3	and	and	CCONJ
ajst-5302	85	4	colors	color	NOUN
ajst-5302	85	5	as	as	SCONJ
ajst-5302	85	6	shown	show	VERB
ajst-5302	85	7	in	in	ADP
ajst-5302	85	8	fig	fig	NOUN
ajst-5302	85	9	.	.	PUNCT
ajst-5302	86	1	5	5	NUM
ajst-5302	86	2	,	,	PUNCT
ajst-5302	86	3	it	it	PRON
ajst-5302	86	4	is	be	AUX
ajst-5302	86	5	not	not	PART
ajst-5302	86	6	difficult	difficult	ADJ
ajst-5302	86	7	to	to	PART
ajst-5302	86	8	find	find	VERB
ajst-5302	86	9	that	that	SCONJ
ajst-5302	86	10	the	the	DET
ajst-5302	86	11	class	class	NOUN
ajst-5302	86	12	label	label	NOUN
ajst-5302	86	13	set	set	VERB
ajst-5302	86	14	by	by	ADP
ajst-5302	86	15	this	this	DET
ajst-5302	86	16	program	program	NOUN
ajst-5302	86	17	includes	include	VERB
ajst-5302	86	18	two	two	NUM
ajst-5302	86	19	categories	category	NOUN
ajst-5302	86	20	of	of	ADP
ajst-5302	86	21	"	"	PUNCT
ajst-5302	86	22	bus	bus	NOUN
ajst-5302	86	23	"	"	PUNCT
ajst-5302	86	24	and	and	CCONJ
ajst-5302	86	25	"	"	PUNCT
ajst-5302	86	26	car	car	NOUN
ajst-5302	86	27	"	"	PUNCT
ajst-5302	86	28	to	to	PART
ajst-5302	86	29	be	be	AUX
ajst-5302	86	30	studied	study	VERB
ajst-5302	86	31	,	,	PUNCT
ajst-5302	86	32	which	which	PRON
ajst-5302	86	33	can	can	AUX
ajst-5302	86	34	be	be	AUX
ajst-5302	86	35	accurately	accurately	ADV
ajst-5302	86	36	detected	detect	VERB
ajst-5302	86	37	,	,	PUNCT
ajst-5302	86	38	and	and	CCONJ
ajst-5302	86	39	then	then	ADV
ajst-5302	86	40	the	the	DET
ajst-5302	86	41	border	border	NOUN
ajst-5302	86	42	color	color	NOUN
ajst-5302	86	43	of	of	ADP
ajst-5302	86	44	the	the	DET
ajst-5302	86	45	object	object	NOUN
ajst-5302	86	46	to	to	PART
ajst-5302	86	47	be	be	AUX
ajst-5302	86	48	detected	detect	VERB
ajst-5302	86	49	is	be	AUX
ajst-5302	86	50	set	set	VERB
ajst-5302	86	51	.	.	PUNCT
ajst-5302	87	1	after	after	ADP
ajst-5302	87	2	setting	set	VERB
ajst-5302	87	3	relevant	relevant	ADJ
ajst-5302	87	4	parameters	parameter	NOUN
ajst-5302	87	5	,	,	PUNCT
ajst-5302	87	6	the	the	DET
ajst-5302	87	7	model	model	NOUN
ajst-5302	87	8	needed	need	VERB
ajst-5302	87	9	for	for	ADP
ajst-5302	87	10	analysis	analysis	NOUN
ajst-5302	87	11	in	in	ADP
ajst-5302	87	12	this	this	DET
ajst-5302	87	13	paper	paper	NOUN
ajst-5302	87	14	needs	need	VERB
ajst-5302	87	15	to	to	PART
ajst-5302	87	16	be	be	AUX
ajst-5302	87	17	loaded	load	VERB
ajst-5302	87	18	.	.	PUNCT
ajst-5302	88	1	the	the	DET
ajst-5302	88	2	program	program	NOUN
ajst-5302	88	3	of	of	ADP
ajst-5302	88	4	loading	load	VERB
ajst-5302	88	5	the	the	DET
ajst-5302	88	6	model	model	NOUN
ajst-5302	88	7	is	be	AUX
ajst-5302	88	8	shown	show	VERB
ajst-5302	88	9	in	in	ADP
ajst-5302	88	10	fig	fig	NOUN
ajst-5302	88	11	.	.	PUNCT
ajst-5302	89	1	6	6	X
ajst-5302	89	2	.	.	X
ajst-5302	89	3	figure	figure	NOUN
ajst-5302	89	4	6	6	NUM
ajst-5302	89	5	.	.	PUNCT
ajst-5302	90	1	loading	load	VERB
ajst-5302	90	2	the	the	DET
ajst-5302	90	3	model	model	NOUN
ajst-5302	90	4	after	after	ADP
ajst-5302	90	5	loading	load	VERB
ajst-5302	90	6	the	the	DET
ajst-5302	90	7	model	model	NOUN
ajst-5302	90	8	,	,	PUNCT
ajst-5302	90	9	you	you	PRON
ajst-5302	90	10	need	need	VERB
ajst-5302	90	11	to	to	PART
ajst-5302	90	12	load	load	VERB
ajst-5302	90	13	the	the	DET
ajst-5302	90	14	query	query	NOUN
ajst-5302	90	15	image	image	NOUN
ajst-5302	90	16	and	and	CCONJ
ajst-5302	90	17	perform	perform	VERB
ajst-5302	90	18	blob	blob	ADJ
ajst-5302	90	19	analysis	analysis	NOUN
ajst-5302	90	20	.	.	PUNCT
ajst-5302	91	1	the	the	DET
ajst-5302	91	2	blob	blob	NOUN
ajst-5302	91	3	feeds	feed	VERB
ajst-5302	91	4	forward	forward	ADV
ajst-5302	91	5	through	through	ADP
ajst-5302	91	6	the	the	DET
ajst-5302	91	7	network	network	NOUN
ajst-5302	91	8	,	,	PUNCT
ajst-5302	91	9	as	as	SCONJ
ajst-5302	91	10	shown	show	VERB
ajst-5302	91	11	in	in	ADP
ajst-5302	91	12	fig	fig	NOUN
ajst-5302	91	13	.	.	PUNCT
ajst-5302	92	1	7	7	X
ajst-5302	92	2	.	.	X
ajst-5302	92	3	blob	blob	ADJ
ajst-5302	92	4	analysis	analysis	NOUN
ajst-5302	92	5	is	be	AUX
ajst-5302	92	6	the	the	DET
ajst-5302	92	7	analysis	analysis	NOUN
ajst-5302	92	8	of	of	ADP
ajst-5302	92	9	the	the	DET
ajst-5302	92	10	connected	connected	ADJ
ajst-5302	92	11	domain	domain	NOUN
ajst-5302	92	12	of	of	ADP
ajst-5302	92	13	the	the	DET
ajst-5302	92	14	same	same	ADJ
ajst-5302	92	15	pixels	pixel	NOUN
ajst-5302	92	16	in	in	ADP
ajst-5302	92	17	the	the	DET
ajst-5302	92	18	image	image	NOUN
ajst-5302	92	19	,	,	PUNCT
ajst-5302	92	20	which	which	PRON
ajst-5302	92	21	is	be	AUX
ajst-5302	92	22	called	call	VERB
ajst-5302	92	23	blob	blob	ADJ
ajst-5302	92	24	.	.	PUNCT
ajst-5302	93	1	blob	blob	ADJ
ajst-5302	93	2	analysis	analysis	NOUN
ajst-5302	93	3	tools	tool	NOUN
ajst-5302	93	4	can	can	AUX
ajst-5302	93	5	isolate	isolate	VERB
ajst-5302	93	6	objects	object	NOUN
ajst-5302	93	7	from	from	ADP
ajst-5302	93	8	the	the	DET
ajst-5302	93	9	background	background	NOUN
ajst-5302	93	10	and	and	CCONJ
ajst-5302	93	11	calculate	calculate	VERB
ajst-5302	93	12	the	the	DET
ajst-5302	93	13	number	number	NOUN
ajst-5302	93	14	,	,	PUNCT
ajst-5302	93	15	location	location	NOUN
ajst-5302	93	16	,	,	PUNCT
ajst-5302	93	17	shape	shape	NOUN
ajst-5302	93	18	,	,	PUNCT
ajst-5302	93	19	orientation	orientation	NOUN
ajst-5302	93	20	,	,	PUNCT
ajst-5302	93	21	and	and	CCONJ
ajst-5302	93	22	size	size	NOUN
ajst-5302	93	23	of	of	ADP
ajst-5302	93	24	objects	object	NOUN
ajst-5302	93	25	,	,	PUNCT
ajst-5302	93	26	as	as	ADV
ajst-5302	93	27	well	well	ADV
ajst-5302	93	28	as	as	ADP
ajst-5302	93	29	provide	provide	VERB
ajst-5302	93	30	a	a	DET
ajst-5302	93	31	topology	topology	NOUN
ajst-5302	93	32	between	between	ADP
ajst-5302	93	33	related	related	ADJ
ajst-5302	93	34	spots	spot	NOUN
ajst-5302	93	35	.	.	PUNCT
ajst-5302	94	1	figure	figure	NOUN
ajst-5302	94	2	7	7	NUM
ajst-5302	94	3	.	.	PUNCT
ajst-5302	94	4	feature	feature	NOUN
ajst-5302	94	5	extraction	extraction	NOUN
ajst-5302	94	6	in	in	ADP
ajst-5302	94	7	fig	fig	NOUN
ajst-5302	94	8	.	.	PUNCT
ajst-5302	95	1	7	7	NUM
ajst-5302	95	2	,	,	PUNCT
ajst-5302	95	3	the	the	DET
ajst-5302	95	4	image	image	NOUN
ajst-5302	95	5	is	be	AUX
ajst-5302	95	6	first	first	ADV
ajst-5302	95	7	loaded	load	VERB
ajst-5302	95	8	,	,	PUNCT
ajst-5302	95	9	then	then	ADV
ajst-5302	95	10	the	the	DET
ajst-5302	95	11	height	height	NOUN
ajst-5302	95	12	and	and	CCONJ
ajst-5302	95	13	width	width	NOUN
ajst-5302	95	14	of	of	ADP
ajst-5302	95	15	the	the	DET
ajst-5302	95	16	image	image	NOUN
ajst-5302	95	17	are	be	AUX
ajst-5302	95	18	extracted	extract	VERB
ajst-5302	95	19	,	,	PUNCT
ajst-5302	95	20	and	and	CCONJ
ajst-5302	95	21	a	a	DET
ajst-5302	95	22	blob	blob	NOUN
ajst-5302	95	23	of	of	ADP
ajst-5302	95	24	300×300	300×300	NUM
ajst-5302	95	25	pixels	pixel	NOUN
ajst-5302	95	26	is	be	AUX
ajst-5302	95	27	calculated	calculate	VERB
ajst-5302	95	28	from	from	ADP
ajst-5302	95	29	the	the	DET
ajst-5302	95	30	image	image	NOUN
ajst-5302	95	31	.	.	PUNCT
ajst-5302	96	1	after	after	SCONJ
ajst-5302	96	2	feature	feature	NOUN
ajst-5302	96	3	extraction	extraction	NOUN
ajst-5302	96	4	is	be	AUX
ajst-5302	96	5	completed	complete	VERB
ajst-5302	96	6	,	,	PUNCT
ajst-5302	96	7	the	the	DET
ajst-5302	96	8	process	process	NOUN
ajst-5302	96	9	of	of	ADP
ajst-5302	96	10	feature	feature	NOUN
ajst-5302	96	11	transmission	transmission	NOUN
ajst-5302	96	12	is	be	AUX
ajst-5302	96	13	followed	follow	VERB
ajst-5302	96	14	,	,	PUNCT
ajst-5302	96	15	as	as	SCONJ
ajst-5302	96	16	shown	show	VERB
ajst-5302	96	17	in	in	ADP
ajst-5302	96	18	fig	fig	NOUN
ajst-5302	96	19	.	.	PUNCT
ajst-5302	97	1	8	8	X
ajst-5302	97	2	.	.	X
ajst-5302	97	3	figure	figure	NOUN
ajst-5302	97	4	8	8	NUM
ajst-5302	97	5	.	.	PUNCT
ajst-5302	98	1	setting	set	VERB
ajst-5302	98	2	the	the	DET
ajst-5302	98	3	forwarding	forward	VERB
ajst-5302	98	4	network	network	NOUN
ajst-5302	98	5	in	in	ADP
ajst-5302	98	6	fig	fig	NOUN
ajst-5302	98	7	.	.	PUNCT
ajst-5302	99	1	8	8	NUM
ajst-5302	99	2	,	,	PUNCT
ajst-5302	99	3	we	we	PRON
ajst-5302	99	4	need	need	VERB
ajst-5302	99	5	to	to	PART
ajst-5302	99	6	set	set	VERB
ajst-5302	99	7	the	the	DET
ajst-5302	99	8	network	network	NOUN
ajst-5302	99	9	input	input	NOUN
ajst-5302	99	10	,	,	PUNCT
ajst-5302	99	11	calculate	calculate	VERB
ajst-5302	99	12	the	the	DET
ajst-5302	99	13	forward	forward	ADJ
ajst-5302	99	14	transmission	transmission	NOUN
ajst-5302	99	15	result	result	NOUN
ajst-5302	99	16	of	of	ADP
ajst-5302	99	17	the	the	DET
ajst-5302	99	18	input	input	NOUN
ajst-5302	99	19	and	and	CCONJ
ajst-5302	99	20	store	store	VERB
ajst-5302	99	21	the	the	DET
ajst-5302	99	22	result	result	NOUN
ajst-5302	99	23	as	as	ADP
ajst-5302	99	24	detection	detection	NOUN
ajst-5302	99	25	.	.	PUNCT
ajst-5302	100	1	the	the	DET
ajst-5302	100	2	running	running	ADJ
ajst-5302	100	3	time	time	NOUN
ajst-5302	100	4	of	of	ADP
ajst-5302	100	5	the	the	DET
ajst-5302	100	6	program	program	NOUN
ajst-5302	100	7	here	here	ADV
ajst-5302	100	8	is	be	AUX
ajst-5302	100	9	closely	closely	ADV
ajst-5302	100	10	related	relate	VERB
ajst-5302	100	11	to	to	ADP
ajst-5302	100	12	the	the	DET
ajst-5302	100	13	size	size	NOUN
ajst-5302	100	14	of	of	ADP
ajst-5302	100	15	the	the	DET
ajst-5302	100	16	input	input	NOUN
ajst-5302	100	17	features	feature	NOUN
ajst-5302	100	18	and	and	CCONJ
ajst-5302	100	19	the	the	DET
ajst-5302	100	20	model	model	NOUN
ajst-5302	100	21	chosen	choose	VERB
ajst-5302	100	22	,	,	PUNCT
ajst-5302	100	23	but	but	CCONJ
ajst-5302	100	24	the	the	DET
ajst-5302	100	25	method	method	NOUN
ajst-5302	100	26	chosen	choose	VERB
ajst-5302	100	27	in	in	ADP
ajst-5302	100	28	this	this	DET
ajst-5302	100	29	paper	paper	NOUN
ajst-5302	100	30	can	can	AUX
ajst-5302	100	31	be	be	AUX
ajst-5302	100	32	implemented	implement	VERB
ajst-5302	100	33	on	on	ADP
ajst-5302	100	34	ordinary	ordinary	ADJ
ajst-5302	100	35	cpu	cpu	NOUN
ajst-5302	100	36	without	without	ADP
ajst-5302	100	37	gpu	gpu	PROPN
ajst-5302	100	38	intervention	intervention	NOUN
ajst-5302	100	39	.	.	PUNCT
ajst-5302	101	1	after	after	SCONJ
ajst-5302	101	2	all	all	DET
ajst-5302	101	3	configurations	configuration	NOUN
ajst-5302	101	4	are	be	AUX
ajst-5302	101	5	complete	complete	ADJ
ajst-5302	101	6	,	,	PUNCT
ajst-5302	101	7	you	you	PRON
ajst-5302	101	8	need	need	VERB
ajst-5302	101	9	to	to	PART
ajst-5302	101	10	implement	implement	VERB
ajst-5302	101	11	cyclic	cyclic	ADJ
ajst-5302	101	12	detection	detection	NOUN
ajst-5302	101	13	and	and	CCONJ
ajst-5302	101	14	determine	determine	VERB
ajst-5302	101	15	the	the	DET
ajst-5302	101	16	location	location	NOUN
ajst-5302	101	17	of	of	ADP
ajst-5302	101	18	the	the	DET
ajst-5302	101	19	detected	detect	VERB
ajst-5302	101	20	object	object	NOUN
ajst-5302	101	21	.	.	PUNCT
ajst-5302	102	1	fig	fig	NOUN
ajst-5302	102	2	.	.	PUNCT
ajst-5302	103	1	9	9	NUM
ajst-5302	103	2	shows	show	VERB
ajst-5302	103	3	the	the	DET
ajst-5302	103	4	detection	detection	NOUN
ajst-5302	103	5	process	process	NOUN
ajst-5302	103	6	.	.	PUNCT
ajst-5302	104	1	42	42	NUM
ajst-5302	104	2	figure	figure	NOUN
ajst-5302	104	3	9	9	NUM
ajst-5302	104	4	.	.	PUNCT
ajst-5302	104	5	target	target	NOUN
ajst-5302	104	6	detection	detection	NOUN
ajst-5302	104	7	in	in	ADP
ajst-5302	104	8	fig	fig	NOUN
ajst-5302	104	9	.	.	PUNCT
ajst-5302	105	1	9	9	NUM
ajst-5302	105	2	we	we	PRON
ajst-5302	105	3	first	first	ADJ
ajst-5302	105	4	loop	loop	VERB
ajst-5302	105	5	through	through	ADP
ajst-5302	105	6	our	our	PRON
ajst-5302	105	7	detection	detection	NOUN
ajst-5302	105	8	,	,	PUNCT
ajst-5302	105	9	remembering	remember	VERB
ajst-5302	105	10	that	that	SCONJ
ajst-5302	105	11	multiple	multiple	ADJ
ajst-5302	105	12	objects	object	NOUN
ajst-5302	105	13	can	can	AUX
ajst-5302	105	14	be	be	AUX
ajst-5302	105	15	detected	detect	VERB
ajst-5302	105	16	in	in	ADP
ajst-5302	105	17	a	a	DET
ajst-5302	105	18	single	single	ADJ
ajst-5302	105	19	image	image	NOUN
ajst-5302	105	20	,	,	PUNCT
ajst-5302	105	21	in	in	ADP
ajst-5302	105	22	addition	addition	NOUN
ajst-5302	105	23	to	to	ADP
ajst-5302	105	24	checking	check	VERB
ajst-5302	105	25	the	the	DET
ajst-5302	105	26	confidence	confidence	NOUN
ajst-5302	105	27	(	(	PUNCT
ajst-5302	105	28	i.e.	i.e.	X
ajst-5302	105	29	,	,	PUNCT
ajst-5302	105	30	probability	probability	NOUN
ajst-5302	105	31	)	)	PUNCT
ajst-5302	105	32	associated	associate	VERB
ajst-5302	105	33	with	with	ADP
ajst-5302	105	34	each	each	DET
ajst-5302	105	35	detection	detection	NOUN
ajst-5302	105	36	.	.	PUNCT
ajst-5302	106	1	if	if	SCONJ
ajst-5302	106	2	the	the	DET
ajst-5302	106	3	confidence	confidence	NOUN
ajst-5302	106	4	level	level	NOUN
ajst-5302	106	5	is	be	AUX
ajst-5302	106	6	high	high	ADJ
ajst-5302	106	7	enough	enough	ADV
ajst-5302	106	8	(	(	PUNCT
ajst-5302	106	9	that	that	PRON
ajst-5302	106	10	is	is	ADV
ajst-5302	106	11	,	,	PUNCT
ajst-5302	106	12	above	above	ADP
ajst-5302	106	13	the	the	DET
ajst-5302	106	14	threshold	threshold	NOUN
ajst-5302	106	15	)	)	PUNCT
ajst-5302	106	16	,	,	PUNCT
ajst-5302	106	17	then	then	ADV
ajst-5302	106	18	we	we	PRON
ajst-5302	106	19	display	display	VERB
ajst-5302	106	20	the	the	DET
ajst-5302	106	21	prediction	prediction	NOUN
ajst-5302	106	22	in	in	ADP
ajst-5302	106	23	the	the	DET
ajst-5302	106	24	terminal	terminal	NOUN
ajst-5302	106	25	and	and	CCONJ
ajst-5302	106	26	draw	draw	VERB
ajst-5302	106	27	the	the	DET
ajst-5302	106	28	prediction	prediction	NOUN
ajst-5302	106	29	on	on	ADP
ajst-5302	106	30	the	the	DET
ajst-5302	106	31	image	image	NOUN
ajst-5302	106	32	using	use	VERB
ajst-5302	106	33	text	text	NOUN
ajst-5302	106	34	and	and	CCONJ
ajst-5302	106	35	color	color	NOUN
ajst-5302	106	36	bounding	bounding	NOUN
ajst-5302	106	37	boxes	box	NOUN
ajst-5302	106	38	.	.	PUNCT
ajst-5302	107	1	the	the	DET
ajst-5302	107	2	specific	specific	ADJ
ajst-5302	107	3	detection	detection	NOUN
ajst-5302	107	4	steps	step	NOUN
ajst-5302	107	5	are	be	AUX
ajst-5302	107	6	as	as	SCONJ
ajst-5302	107	7	follows	follow	VERB
ajst-5302	107	8	:	:	PUNCT
ajst-5302	107	9	step	step	NOUN
ajst-5302	107	10	p1	p1	PROPN
ajst-5302	107	11	:	:	PUNCT
ajst-5302	107	12	loop	loop	NOUN
ajst-5302	107	13	test	test	NOUN
ajst-5302	107	14	the	the	DET
ajst-5302	107	15	object	object	NOUN
ajst-5302	107	16	to	to	PART
ajst-5302	107	17	be	be	AUX
ajst-5302	107	18	tested	test	VERB
ajst-5302	107	19	,	,	PUNCT
ajst-5302	107	20	and	and	CCONJ
ajst-5302	107	21	extract	extract	VERB
ajst-5302	107	22	the	the	DET
ajst-5302	107	23	confidence	confidence	NOUN
ajst-5302	107	24	value	value	NOUN
ajst-5302	107	25	using	use	VERB
ajst-5302	107	26	the	the	DET
ajst-5302	107	27	confidence	confidence	NOUN
ajst-5302	107	28	function	function	NOUN
ajst-5302	107	29	;	;	PUNCT
ajst-5302	107	30	step2	step2	PROPN
ajst-5302	107	31	:	:	PUNCT
ajst-5302	107	32	judge	judge	VERB
ajst-5302	107	33	the	the	DET
ajst-5302	107	34	size	size	NOUN
ajst-5302	107	35	of	of	ADP
ajst-5302	107	36	the	the	DET
ajst-5302	107	37	confidence	confidence	NOUN
ajst-5302	107	38	value	value	NOUN
ajst-5302	107	39	.	.	PUNCT
ajst-5302	108	1	if	if	SCONJ
ajst-5302	108	2	the	the	DET
ajst-5302	108	3	confidence	confidence	NOUN
ajst-5302	108	4	value	value	NOUN
ajst-5302	108	5	is	be	AUX
ajst-5302	108	6	greater	great	ADJ
ajst-5302	108	7	than	than	ADP
ajst-5302	108	8	the	the	DET
ajst-5302	108	9	minimum	minimum	NOUN
ajst-5302	108	10	threshold	threshold	NOUN
ajst-5302	108	11	set	set	NOUN
ajst-5302	108	12	,	,	PUNCT
ajst-5302	108	13	extract	extract	VERB
ajst-5302	108	14	the	the	DET
ajst-5302	108	15	class	class	NOUN
ajst-5302	108	16	index	index	NOUN
ajst-5302	108	17	label	label	NOUN
ajst-5302	108	18	and	and	CCONJ
ajst-5302	108	19	calculate	calculate	VERB
ajst-5302	108	20	the	the	DET
ajst-5302	108	21	boundary	boundary	ADJ
ajst-5302	108	22	box	box	NOUN
ajst-5302	108	23	around	around	ADP
ajst-5302	108	24	the	the	DET
ajst-5302	108	25	detected	detect	VERB
ajst-5302	108	26	object	object	NOUN
ajst-5302	108	27	;	;	PUNCT
ajst-5302	108	28	step3	step3	PROPN
ajst-5302	108	29	:	:	PUNCT
ajst-5302	108	30	after	after	ADP
ajst-5302	108	31	calculating	calculate	VERB
ajst-5302	108	32	the	the	DET
ajst-5302	108	33	value	value	NOUN
ajst-5302	108	34	of	of	ADP
ajst-5302	108	35	the	the	DET
ajst-5302	108	36	boundary	boundary	ADJ
ajst-5302	108	37	box	box	NOUN
ajst-5302	108	38	,	,	PUNCT
ajst-5302	108	39	extract	extract	VERB
ajst-5302	108	40	the	the	DET
ajst-5302	108	41	coordinate	coordinate	NOUN
ajst-5302	108	42	value	value	NOUN
ajst-5302	108	43	of	of	ADP
ajst-5302	108	44	the	the	DET
ajst-5302	108	45	border	border	NOUN
ajst-5302	108	46	,	,	PUNCT
ajst-5302	108	47	and	and	CCONJ
ajst-5302	108	48	use	use	VERB
ajst-5302	108	49	it	it	PRON
ajst-5302	108	50	to	to	PART
ajst-5302	108	51	draw	draw	VERB
ajst-5302	108	52	a	a	DET
ajst-5302	108	53	rectangle	rectangle	NOUN
ajst-5302	108	54	and	and	CCONJ
ajst-5302	108	55	display	display	NOUN
ajst-5302	108	56	text	text	NOUN
ajst-5302	108	57	;	;	PUNCT
ajst-5302	108	58	step4	step4	PROPN
ajst-5302	108	59	:	:	PUNCT
ajst-5302	108	60	the	the	DET
ajst-5302	108	61	display	display	NOUN
ajst-5302	108	62	text	text	NOUN
ajst-5302	108	63	needs	need	VERB
ajst-5302	108	64	to	to	PART
ajst-5302	108	65	be	be	AUX
ajst-5302	108	66	constructed	construct	VERB
ajst-5302	108	67	by	by	ADP
ajst-5302	108	68	itself	itself	PRON
ajst-5302	108	69	,	,	PUNCT
ajst-5302	108	70	so	so	ADV
ajst-5302	108	71	next	next	ADV
ajst-5302	108	72	it	it	PRON
ajst-5302	108	73	needs	need	VERB
ajst-5302	108	74	to	to	PART
ajst-5302	108	75	build	build	VERB
ajst-5302	108	76	a	a	DET
ajst-5302	108	77	text	text	NOUN
ajst-5302	108	78	label	label	NOUN
ajst-5302	108	79	containing	contain	VERB
ajst-5302	108	80	the	the	DET
ajst-5302	108	81	class	class	NOUN
ajst-5302	108	82	name	name	NOUN
ajst-5302	108	83	and	and	CCONJ
ajst-5302	108	84	confidence	confidence	NOUN
ajst-5302	108	85	;	;	PUNCT
ajst-5302	108	86	step5	step5	PROPN
ajst-5302	108	87	:	:	PUNCT
ajst-5302	108	88	use	use	VERB
ajst-5302	108	89	the	the	DET
ajst-5302	108	90	label	label	NOUN
ajst-5302	108	91	,	,	PUNCT
ajst-5302	108	92	print	print	VERB
ajst-5302	108	93	it	it	PRON
ajst-5302	108	94	to	to	ADP
ajst-5302	108	95	the	the	DET
ajst-5302	108	96	terminal	terminal	NOUN
ajst-5302	108	97	for	for	ADP
ajst-5302	108	98	display	display	NOUN
ajst-5302	108	99	,	,	PUNCT
ajst-5302	108	100	and	and	CCONJ
ajst-5302	108	101	then	then	ADV
ajst-5302	108	102	use	use	VERB
ajst-5302	108	103	the	the	DET
ajst-5302	108	104	coordinates	coordinate	NOUN
ajst-5302	108	105	extracted	extract	VERB
ajst-5302	108	106	before	before	ADV
ajst-5302	108	107	to	to	PART
ajst-5302	108	108	draw	draw	VERB
ajst-5302	108	109	a	a	DET
ajst-5302	108	110	color	color	NOUN
ajst-5302	108	111	rectangle	rectangle	NOUN
ajst-5302	108	112	around	around	ADP
ajst-5302	108	113	the	the	DET
ajst-5302	108	114	object	object	NOUN
ajst-5302	108	115	;	;	PUNCT
ajst-5302	108	116	step6	step6	NOUN
ajst-5302	108	117	:	:	PUNCT
ajst-5302	108	118	set	set	VERB
ajst-5302	108	119	the	the	DET
ajst-5302	108	120	label	label	NOUN
ajst-5302	108	121	display	display	NOUN
ajst-5302	108	122	position	position	NOUN
ajst-5302	108	123	,	,	PUNCT
ajst-5302	108	124	which	which	PRON
ajst-5302	108	125	is	be	AUX
ajst-5302	108	126	not	not	PART
ajst-5302	108	127	important	important	ADJ
ajst-5302	108	128	in	in	ADP
ajst-5302	108	129	the	the	DET
ajst-5302	108	130	whole	whole	ADJ
ajst-5302	108	131	detection	detection	NOUN
ajst-5302	108	132	process	process	NOUN
ajst-5302	108	133	.	.	PUNCT
ajst-5302	109	1	under	under	ADP
ajst-5302	109	2	normal	normal	ADJ
ajst-5302	109	3	circumstances	circumstance	NOUN
ajst-5302	109	4	,	,	PUNCT
ajst-5302	109	5	we	we	PRON
ajst-5302	109	6	want	want	VERB
ajst-5302	109	7	the	the	DET
ajst-5302	109	8	label	label	NOUN
ajst-5302	109	9	to	to	PART
ajst-5302	109	10	be	be	AUX
ajst-5302	109	11	displayed	display	VERB
ajst-5302	109	12	above	above	ADP
ajst-5302	109	13	the	the	DET
ajst-5302	109	14	rectangle	rectangle	NOUN
ajst-5302	109	15	,	,	PUNCT
ajst-5302	109	16	but	but	CCONJ
ajst-5302	109	17	if	if	SCONJ
ajst-5302	109	18	there	there	PRON
ajst-5302	109	19	is	be	VERB
ajst-5302	109	20	no	no	DET
ajst-5302	109	21	space	space	NOUN
ajst-5302	109	22	,	,	PUNCT
ajst-5302	109	23	we	we	PRON
ajst-5302	109	24	will	will	AUX
ajst-5302	109	25	display	display	VERB
ajst-5302	109	26	it	it	PRON
ajst-5302	109	27	below	below	ADP
ajst-5302	109	28	the	the	DET
ajst-5302	109	29	top	top	NOUN
ajst-5302	109	30	of	of	ADP
ajst-5302	109	31	the	the	DET
ajst-5302	109	32	rectangle	rectangle	NOUN
ajst-5302	109	33	.	.	PUNCT
ajst-5302	110	1	the	the	DET
ajst-5302	110	2	setting	setting	NOUN
ajst-5302	110	3	here	here	ADV
ajst-5302	110	4	can	can	AUX
ajst-5302	110	5	be	be	AUX
ajst-5302	110	6	set	set	VERB
ajst-5302	110	7	according	accord	VERB
ajst-5302	110	8	to	to	ADP
ajst-5302	110	9	the	the	DET
ajst-5302	110	10	preferences	preference	NOUN
ajst-5302	110	11	of	of	ADP
ajst-5302	110	12	scholars	scholar	NOUN
ajst-5302	110	13	.	.	PUNCT
ajst-5302	111	1	step7	step7	VERB
ajst-5302	111	2	:	:	PUNCT
ajst-5302	111	3	finally	finally	ADV
ajst-5302	111	4	,	,	PUNCT
ajst-5302	111	5	use	use	VERB
ajst-5302	111	6	the	the	DET
ajst-5302	111	7	value	value	NOUN
ajst-5302	111	8	just	just	ADV
ajst-5302	111	9	calculated	calculate	VERB
ajst-5302	111	10	to	to	PART
ajst-5302	111	11	cover	cover	VERB
ajst-5302	111	12	the	the	DET
ajst-5302	111	13	color	color	NOUN
ajst-5302	111	14	text	text	NOUN
ajst-5302	111	15	on	on	ADP
ajst-5302	111	16	the	the	DET
ajst-5302	111	17	image	image	NOUN
ajst-5302	111	18	.	.	PUNCT
ajst-5302	112	1	after	after	SCONJ
ajst-5302	112	2	all	all	DET
ajst-5302	112	3	the	the	DET
ajst-5302	112	4	detection	detection	NOUN
ajst-5302	112	5	steps	step	NOUN
ajst-5302	112	6	are	be	AUX
ajst-5302	112	7	completed	complete	VERB
ajst-5302	112	8	,	,	PUNCT
ajst-5302	112	9	the	the	DET
ajst-5302	112	10	detected	detect	VERB
ajst-5302	112	11	images	image	NOUN
ajst-5302	112	12	need	need	VERB
ajst-5302	112	13	to	to	PART
ajst-5302	112	14	be	be	AUX
ajst-5302	112	15	displayed	display	VERB
ajst-5302	112	16	to	to	ADP
ajst-5302	112	17	the	the	DET
ajst-5302	112	18	readers	reader	NOUN
ajst-5302	112	19	,	,	PUNCT
ajst-5302	112	20	as	as	SCONJ
ajst-5302	112	21	shown	show	VERB
ajst-5302	112	22	in	in	ADP
ajst-5302	112	23	fig	fig	NOUN
ajst-5302	112	24	.	.	PUNCT
ajst-5302	113	1	10	10	NUM
ajst-5302	113	2	.	.	PUNCT
ajst-5302	113	3	figure	figure	NOUN
ajst-5302	113	4	10	10	NUM
ajst-5302	113	5	.	.	PUNCT
ajst-5302	114	1	detection	detection	NOUN
ajst-5302	114	2	result	result	VERB
ajst-5302	114	3	so	so	ADV
ajst-5302	114	4	far	far	ADV
ajst-5302	114	5	,	,	PUNCT
ajst-5302	114	6	the	the	DET
ajst-5302	114	7	opencv	opencv	PROPN
ajst-5302	114	8	-	-	PUNCT
ajst-5302	114	9	based	base	VERB
ajst-5302	114	10	target	target	NOUN
ajst-5302	114	11	detection	detection	NOUN
ajst-5302	114	12	system	system	NOUN
ajst-5302	114	13	has	have	AUX
ajst-5302	114	14	completed	complete	VERB
ajst-5302	114	15	all	all	DET
ajst-5302	114	16	the	the	DET
ajst-5302	114	17	functions	function	NOUN
ajst-5302	114	18	of	of	ADP
ajst-5302	114	19	static	static	ADJ
ajst-5302	114	20	detection	detection	NOUN
ajst-5302	114	21	.	.	PUNCT
ajst-5302	115	1	for	for	ADP
ajst-5302	115	2	the	the	DET
ajst-5302	115	3	dynamic	dynamic	ADJ
ajst-5302	115	4	detection	detection	NOUN
ajst-5302	115	5	of	of	ADP
ajst-5302	115	6	detection	detection	NOUN
ajst-5302	115	7	objects	object	NOUN
ajst-5302	115	8	,	,	PUNCT
ajst-5302	115	9	it	it	PRON
ajst-5302	115	10	only	only	ADV
ajst-5302	115	11	needs	need	VERB
ajst-5302	115	12	to	to	PART
ajst-5302	115	13	change	change	VERB
ajst-5302	115	14	image	image	NOUN
ajst-5302	115	15	to	to	ADP
ajst-5302	115	16	video	video	NOUN
ajst-5302	115	17	before	before	ADP
ajst-5302	115	18	the	the	DET
ajst-5302	115	19	program	program	NOUN
ajst-5302	115	20	.	.	PUNCT
ajst-5302	116	1	the	the	DET
ajst-5302	116	2	target	target	NOUN
ajst-5302	116	3	detection	detection	NOUN
ajst-5302	116	4	results	result	VERB
ajst-5302	116	5	under	under	ADP
ajst-5302	116	6	the	the	DET
ajst-5302	116	7	above	above	ADJ
ajst-5302	116	8	two	two	NUM
ajst-5302	116	9	states	state	NOUN
ajst-5302	116	10	will	will	AUX
ajst-5302	116	11	be	be	AUX
ajst-5302	116	12	shown	show	VERB
ajst-5302	116	13	in	in	ADP
ajst-5302	116	14	the	the	DET
ajst-5302	116	15	next	next	ADJ
ajst-5302	116	16	chapter	chapter	NOUN
ajst-5302	116	17	.	.	PUNCT
ajst-5302	117	1	3.2	3.2	NUM
ajst-5302	117	2	.	.	PUNCT
ajst-5302	118	1	vehicle	vehicle	NOUN
ajst-5302	118	2	detection	detection	NOUN
ajst-5302	118	3	analysis	analysis	NOUN
ajst-5302	118	4	based	base	VERB
ajst-5302	118	5	on	on	ADP
ajst-5302	118	6	opencv	opencv	PROPN
ajst-5302	118	7	in	in	ADP
ajst-5302	118	8	general	general	ADJ
ajst-5302	118	9	,	,	PUNCT
ajst-5302	118	10	the	the	DET
ajst-5302	118	11	vehicle	vehicle	NOUN
ajst-5302	118	12	state	state	NOUN
ajst-5302	118	13	is	be	AUX
ajst-5302	118	14	divided	divide	VERB
ajst-5302	118	15	into	into	ADP
ajst-5302	118	16	two	two	NUM
ajst-5302	118	17	states	state	NOUN
ajst-5302	118	18	:	:	PUNCT
ajst-5302	118	19	moving	move	VERB
ajst-5302	118	20	and	and	CCONJ
ajst-5302	118	21	stationary	stationary	ADJ
ajst-5302	118	22	.	.	PUNCT
ajst-5302	119	1	a	a	DET
ajst-5302	119	2	relatively	relatively	ADV
ajst-5302	119	3	complete	complete	ADJ
ajst-5302	119	4	vehicle	vehicle	NOUN
ajst-5302	119	5	recognition	recognition	NOUN
ajst-5302	119	6	system	system	NOUN
ajst-5302	119	7	based	base	VERB
ajst-5302	119	8	on	on	ADP
ajst-5302	119	9	computer	computer	NOUN
ajst-5302	119	10	vision	vision	NOUN
ajst-5302	119	11	consists	consist	VERB
ajst-5302	119	12	of	of	ADP
ajst-5302	119	13	five	five	NUM
ajst-5302	119	14	parts	part	NOUN
ajst-5302	119	15	:	:	PUNCT
ajst-5302	119	16	vehicle	vehicle	NOUN
ajst-5302	119	17	image	image	NOUN
ajst-5302	119	18	information	information	NOUN
ajst-5302	119	19	acquisition	acquisition	NOUN
ajst-5302	119	20	,	,	PUNCT
ajst-5302	119	21	vehicle	vehicle	NOUN
ajst-5302	119	22	image	image	NOUN
ajst-5302	119	23	preprocessing	preprocessing	NOUN
ajst-5302	119	24	,	,	PUNCT
ajst-5302	119	25	vehicle	vehicle	NOUN
ajst-5302	119	26	image	image	NOUN
ajst-5302	119	27	segmentation	segmentation	NOUN
ajst-5302	119	28	,	,	PUNCT
ajst-5302	119	29	vehicle	vehicle	NOUN
ajst-5302	119	30	image	image	NOUN
ajst-5302	119	31	feature	feature	NOUN
ajst-5302	119	32	extraction	extraction	NOUN
ajst-5302	119	33	,	,	PUNCT
ajst-5302	119	34	vehicle	vehicle	NOUN
ajst-5302	119	35	recognition	recognition	NOUN
ajst-5302	119	36	.	.	PUNCT
ajst-5302	120	1	the	the	DET
ajst-5302	120	2	above	above	ADJ
ajst-5302	120	3	5	5	NUM
ajst-5302	120	4	parts	part	NOUN
ajst-5302	120	5	are	be	AUX
ajst-5302	120	6	equally	equally	ADV
ajst-5302	120	7	applicable	applicable	ADJ
ajst-5302	120	8	to	to	ADP
ajst-5302	120	9	both	both	PRON
ajst-5302	120	10	moving	move	VERB
ajst-5302	120	11	and	and	CCONJ
ajst-5302	120	12	stationary	stationary	ADJ
ajst-5302	120	13	vehicles	vehicle	NOUN
ajst-5302	120	14	.	.	PUNCT
ajst-5302	121	1	vehicle	vehicle	NOUN
ajst-5302	121	2	image	image	NOUN
ajst-5302	121	3	information	information	NOUN
ajst-5302	121	4	can	can	AUX
ajst-5302	121	5	be	be	AUX
ajst-5302	121	6	captured	capture	VERB
ajst-5302	121	7	by	by	ADP
ajst-5302	121	8	high	high	ADJ
ajst-5302	121	9	-	-	PUNCT
ajst-5302	121	10	speed	speed	NOUN
ajst-5302	121	11	camera	camera	NOUN
ajst-5302	121	12	or	or	CCONJ
ajst-5302	121	13	video	video	NOUN
ajst-5302	121	14	data	datum	NOUN
ajst-5302	121	15	stream	stream	NOUN
ajst-5302	121	16	collected	collect	VERB
ajst-5302	121	17	by	by	ADP
ajst-5302	121	18	high	high	ADJ
ajst-5302	121	19	-	-	PUNCT
ajst-5302	121	20	definition	definition	NOUN
ajst-5302	121	21	camera	camera	NOUN
ajst-5302	121	22	.	.	PUNCT
ajst-5302	122	1	after	after	ADP
ajst-5302	122	2	the	the	DET
ajst-5302	122	3	acquisition	acquisition	NOUN
ajst-5302	122	4	of	of	ADP
ajst-5302	122	5	image	image	NOUN
ajst-5302	122	6	information	information	NOUN
ajst-5302	122	7	,	,	PUNCT
ajst-5302	122	8	it	it	PRON
ajst-5302	122	9	is	be	AUX
ajst-5302	122	10	necessary	necessary	ADJ
ajst-5302	122	11	to	to	PART
ajst-5302	122	12	preprocess	preprocess	VERB
ajst-5302	122	13	the	the	DET
ajst-5302	122	14	image	image	NOUN
ajst-5302	122	15	information	information	NOUN
ajst-5302	122	16	.	.	PUNCT
ajst-5302	123	1	the	the	DET
ajst-5302	123	2	processing	processing	NOUN
ajst-5302	123	3	content	content	NOUN
ajst-5302	123	4	includes	include	VERB
ajst-5302	123	5	information	information	NOUN
ajst-5302	123	6	denoising	denoise	VERB
ajst-5302	123	7	processing	processing	NOUN
ajst-5302	123	8	and	and	CCONJ
ajst-5302	123	9	image	image	NOUN
ajst-5302	123	10	effect	effect	NOUN
ajst-5302	123	11	enhancement	enhancement	NOUN
ajst-5302	123	12	processing	processing	NOUN
ajst-5302	123	13	.	.	PUNCT
ajst-5302	124	1	after	after	ADP
ajst-5302	124	2	the	the	DET
ajst-5302	124	3	43	43	NUM
ajst-5302	124	4	completion	completion	NOUN
ajst-5302	124	5	of	of	ADP
ajst-5302	124	6	image	image	NOUN
ajst-5302	124	7	preprocessing	preprocessing	NOUN
ajst-5302	124	8	,	,	PUNCT
ajst-5302	124	9	the	the	DET
ajst-5302	124	10	image	image	NOUN
ajst-5302	124	11	needs	need	VERB
ajst-5302	124	12	to	to	PART
ajst-5302	124	13	be	be	AUX
ajst-5302	124	14	segmented	segment	VERB
ajst-5302	124	15	.	.	PUNCT
ajst-5302	125	1	the	the	DET
ajst-5302	125	2	purpose	purpose	NOUN
ajst-5302	125	3	of	of	ADP
ajst-5302	125	4	image	image	NOUN
ajst-5302	125	5	segmentation	segmentation	NOUN
ajst-5302	125	6	is	be	AUX
ajst-5302	125	7	to	to	PART
ajst-5302	125	8	determine	determine	VERB
ajst-5302	125	9	whether	whether	SCONJ
ajst-5302	125	10	there	there	PRON
ajst-5302	125	11	is	be	VERB
ajst-5302	125	12	a	a	DET
ajst-5302	125	13	vehicle	vehicle	NOUN
ajst-5302	125	14	in	in	ADP
ajst-5302	125	15	it	it	PRON
ajst-5302	125	16	.	.	PUNCT
ajst-5302	126	1	if	if	SCONJ
ajst-5302	126	2	there	there	PRON
ajst-5302	126	3	is	be	VERB
ajst-5302	126	4	a	a	DET
ajst-5302	126	5	vehicle	vehicle	NOUN
ajst-5302	126	6	,	,	PUNCT
ajst-5302	126	7	the	the	DET
ajst-5302	126	8	next	next	ADJ
ajst-5302	126	9	step	step	NOUN
ajst-5302	126	10	of	of	ADP
ajst-5302	126	11	feature	feature	NOUN
ajst-5302	126	12	extraction	extraction	NOUN
ajst-5302	126	13	can	can	AUX
ajst-5302	126	14	be	be	AUX
ajst-5302	126	15	carried	carry	VERB
ajst-5302	126	16	out	out	ADP
ajst-5302	126	17	.	.	PUNCT
ajst-5302	127	1	classification	classification	NOUN
ajst-5302	127	2	processing	processing	NOUN
ajst-5302	127	3	mainly	mainly	ADV
ajst-5302	127	4	includes	include	VERB
ajst-5302	127	5	establishing	establish	VERB
ajst-5302	127	6	vehicle	vehicle	NOUN
ajst-5302	127	7	classification	classification	NOUN
ajst-5302	127	8	model	model	NOUN
ajst-5302	127	9	according	accord	VERB
ajst-5302	127	10	to	to	ADP
ajst-5302	127	11	vehicle	vehicle	NOUN
ajst-5302	127	12	sample	sample	NOUN
ajst-5302	127	13	information	information	NOUN
ajst-5302	127	14	,	,	PUNCT
ajst-5302	127	15	and	and	CCONJ
ajst-5302	127	16	adjusting	adjust	VERB
ajst-5302	127	17	relevant	relevant	ADJ
ajst-5302	127	18	parameters	parameter	NOUN
ajst-5302	127	19	in	in	ADP
ajst-5302	127	20	the	the	DET
ajst-5302	127	21	model	model	NOUN
ajst-5302	127	22	,	,	PUNCT
ajst-5302	127	23	and	and	CCONJ
ajst-5302	127	24	finally	finally	ADV
ajst-5302	127	25	realizing	realize	VERB
ajst-5302	127	26	the	the	DET
ajst-5302	127	27	whole	whole	ADJ
ajst-5302	127	28	vehicle	vehicle	NOUN
ajst-5302	127	29	identification	identification	NOUN
ajst-5302	127	30	work	work	NOUN
ajst-5302	127	31	based	base	VERB
ajst-5302	127	32	on	on	ADP
ajst-5302	127	33	this	this	PRON
ajst-5302	127	34	.	.	PUNCT
ajst-5302	128	1	4	4	X
ajst-5302	128	2	.	.	X
ajst-5302	128	3	the	the	DET
ajst-5302	128	4	case	case	NOUN
ajst-5302	128	5	analysis	analysis	NOUN
ajst-5302	128	6	of	of	ADP
ajst-5302	128	7	vehicle	vehicle	NOUN
ajst-5302	128	8	detection	detection	NOUN
ajst-5302	128	9	4.1	4.1	NUM
ajst-5302	128	10	.	.	PUNCT
ajst-5302	129	1	static	static	ADJ
ajst-5302	129	2	vehicle	vehicle	NOUN
ajst-5302	129	3	image	image	NOUN
ajst-5302	129	4	recognition	recognition	NOUN
ajst-5302	129	5	firstly	firstly	ADV
ajst-5302	129	6	,	,	PUNCT
ajst-5302	129	7	the	the	DET
ajst-5302	129	8	vehicle	vehicle	NOUN
ajst-5302	129	9	image	image	NOUN
ajst-5302	129	10	is	be	AUX
ajst-5302	129	11	detected	detect	VERB
ajst-5302	129	12	according	accord	VERB
ajst-5302	129	13	to	to	ADP
ajst-5302	129	14	the	the	DET
ajst-5302	129	15	requirements	requirement	NOUN
ajst-5302	129	16	of	of	ADP
ajst-5302	129	17	this	this	DET
ajst-5302	129	18	paper	paper	NOUN
ajst-5302	129	19	.	.	PUNCT
ajst-5302	130	1	the	the	DET
ajst-5302	130	2	original	original	ADJ
ajst-5302	130	3	image	image	NOUN
ajst-5302	130	4	and	and	CCONJ
ajst-5302	130	5	detection	detection	NOUN
ajst-5302	130	6	results	result	NOUN
ajst-5302	130	7	are	be	AUX
ajst-5302	130	8	shown	show	VERB
ajst-5302	130	9	in	in	ADP
ajst-5302	130	10	fig	fig	NOUN
ajst-5302	130	11	.	.	PUNCT
ajst-5302	131	1	11	11	NUM
ajst-5302	131	2	.	.	PUNCT
ajst-5302	132	1	(	(	PUNCT
ajst-5302	132	2	a	a	X
ajst-5302	132	3	)	)	PUNCT
ajst-5302	132	4	original	original	ADJ
ajst-5302	132	5	drawing	drawing	NOUN
ajst-5302	132	6	of	of	ADP
ajst-5302	132	7	vehicle	vehicle	NOUN
ajst-5302	132	8	(	(	PUNCT
ajst-5302	132	9	b	b	NOUN
ajst-5302	132	10	)	)	PUNCT
ajst-5302	132	11	vehicle	vehicle	NOUN
ajst-5302	132	12	test	test	NOUN
ajst-5302	132	13	result	result	NOUN
ajst-5302	132	14	figure	figure	NOUN
ajst-5302	132	15	11	11	NUM
ajst-5302	132	16	.	.	PUNCT
ajst-5302	133	1	comparison	comparison	NOUN
ajst-5302	133	2	of	of	ADP
ajst-5302	133	3	vehicle	vehicle	NOUN
ajst-5302	133	4	detection	detection	NOUN
ajst-5302	133	5	the	the	DET
ajst-5302	133	6	vehicle	vehicle	NOUN
ajst-5302	133	7	in	in	ADP
ajst-5302	133	8	fig	fig	NOUN
ajst-5302	133	9	.	.	PUNCT
ajst-5302	134	1	11(a	11(a	NUM
ajst-5302	134	2	)	)	PUNCT
ajst-5302	134	3	is	be	AUX
ajst-5302	134	4	detected	detect	VERB
ajst-5302	134	5	,	,	PUNCT
ajst-5302	134	6	and	and	CCONJ
ajst-5302	134	7	the	the	DET
ajst-5302	134	8	results	result	NOUN
ajst-5302	134	9	are	be	AUX
ajst-5302	134	10	shown	show	VERB
ajst-5302	134	11	in	in	ADP
ajst-5302	134	12	fig	fig	NOUN
ajst-5302	134	13	.	.	PUNCT
ajst-5302	135	1	11(b	11(b	NUM
ajst-5302	135	2	)	)	PUNCT
ajst-5302	135	3	.	.	PUNCT
ajst-5302	136	1	it	it	PRON
ajst-5302	136	2	can	can	AUX
ajst-5302	136	3	be	be	AUX
ajst-5302	136	4	seen	see	VERB
ajst-5302	136	5	that	that	SCONJ
ajst-5302	136	6	the	the	DET
ajst-5302	136	7	detection	detection	NOUN
ajst-5302	136	8	algorithm	algorithm	NOUN
ajst-5302	136	9	correctly	correctly	ADV
ajst-5302	136	10	gives	give	VERB
ajst-5302	136	11	the	the	DET
ajst-5302	136	12	frame	frame	NOUN
ajst-5302	136	13	where	where	SCONJ
ajst-5302	136	14	the	the	DET
ajst-5302	136	15	car	car	NOUN
ajst-5302	136	16	is	be	AUX
ajst-5302	136	17	located	locate	VERB
ajst-5302	136	18	,	,	PUNCT
ajst-5302	136	19	and	and	CCONJ
ajst-5302	136	20	the	the	DET
ajst-5302	136	21	detection	detection	NOUN
ajst-5302	136	22	accuracy	accuracy	NOUN
ajst-5302	136	23	rate	rate	NOUN
ajst-5302	136	24	is	be	AUX
ajst-5302	136	25	as	as	ADV
ajst-5302	136	26	high	high	ADJ
ajst-5302	136	27	as	as	ADP
ajst-5302	136	28	99.99	99.99	NUM
ajst-5302	136	29	%	%	NOUN
ajst-5302	136	30	.	.	PUNCT
ajst-5302	137	1	in	in	ADP
ajst-5302	137	2	order	order	NOUN
ajst-5302	137	3	to	to	PART
ajst-5302	137	4	exclude	exclude	VERB
ajst-5302	137	5	accidental	accidental	ADJ
ajst-5302	137	6	circumstances	circumstance	NOUN
ajst-5302	137	7	,	,	PUNCT
ajst-5302	137	8	this	this	DET
ajst-5302	137	9	paper	paper	NOUN
ajst-5302	137	10	will	will	AUX
ajst-5302	137	11	give	give	VERB
ajst-5302	137	12	a	a	DET
ajst-5302	137	13	comparison	comparison	NOUN
ajst-5302	137	14	diagram	diagram	NOUN
ajst-5302	137	15	of	of	ADP
ajst-5302	137	16	another	another	DET
ajst-5302	137	17	group	group	NOUN
ajst-5302	137	18	of	of	ADP
ajst-5302	137	19	test	test	NOUN
ajst-5302	137	20	results	result	NOUN
ajst-5302	137	21	,	,	PUNCT
ajst-5302	137	22	as	as	SCONJ
ajst-5302	137	23	shown	show	VERB
ajst-5302	137	24	in	in	ADP
ajst-5302	137	25	fig	fig	NOUN
ajst-5302	137	26	.	.	PUNCT
ajst-5302	138	1	12	12	NUM
ajst-5302	138	2	.	.	PUNCT
ajst-5302	139	1	(	(	PUNCT
ajst-5302	139	2	a	a	X
ajst-5302	139	3	)	)	PUNCT
ajst-5302	139	4	original	original	ADJ
ajst-5302	139	5	drawing	drawing	NOUN
ajst-5302	139	6	of	of	ADP
ajst-5302	139	7	vehicle	vehicle	NOUN
ajst-5302	139	8	(	(	PUNCT
ajst-5302	139	9	b	b	NOUN
ajst-5302	139	10	)	)	PUNCT
ajst-5302	139	11	vehicle	vehicle	NOUN
ajst-5302	139	12	test	test	NOUN
ajst-5302	139	13	result	result	NOUN
ajst-5302	139	14	figure	figure	NOUN
ajst-5302	139	15	12	12	NUM
ajst-5302	139	16	.	.	PUNCT
ajst-5302	140	1	comparison	comparison	NOUN
ajst-5302	140	2	diagram	diagram	NOUN
ajst-5302	140	3	of	of	ADP
ajst-5302	140	4	vehicle	vehicle	NOUN
ajst-5302	140	5	detection	detection	NOUN
ajst-5302	140	6	in	in	ADP
ajst-5302	140	7	order	order	NOUN
ajst-5302	140	8	to	to	PART
ajst-5302	140	9	show	show	VERB
ajst-5302	140	10	that	that	SCONJ
ajst-5302	140	11	the	the	DET
ajst-5302	140	12	detection	detection	NOUN
ajst-5302	140	13	result	result	VERB
ajst-5302	140	14	in	in	ADP
ajst-5302	140	15	fig	fig	NOUN
ajst-5302	140	16	.	.	PUNCT
ajst-5302	141	1	11	11	NUM
ajst-5302	141	2	is	be	AUX
ajst-5302	141	3	not	not	PART
ajst-5302	141	4	accidental	accidental	ADJ
ajst-5302	141	5	,	,	PUNCT
ajst-5302	141	6	some	some	DET
ajst-5302	141	7	other	other	ADJ
ajst-5302	141	8	background	background	NOUN
ajst-5302	141	9	is	be	AUX
ajst-5302	141	10	added	add	VERB
ajst-5302	141	11	to	to	ADP
ajst-5302	141	12	the	the	DET
ajst-5302	141	13	material	material	NOUN
ajst-5302	141	14	given	give	VERB
ajst-5302	141	15	in	in	ADP
ajst-5302	141	16	fig	fig	NOUN
ajst-5302	141	17	.	.	PUNCT
ajst-5302	142	1	12	12	NUM
ajst-5302	142	2	,	,	PUNCT
ajst-5302	142	3	and	and	CCONJ
ajst-5302	142	4	the	the	DET
ajst-5302	142	5	location	location	NOUN
ajst-5302	142	6	of	of	ADP
ajst-5302	142	7	the	the	DET
ajst-5302	142	8	vehicle	vehicle	NOUN
ajst-5302	142	9	is	be	AUX
ajst-5302	142	10	very	very	ADV
ajst-5302	142	11	far	far	ADV
ajst-5302	142	12	away	away	ADV
ajst-5302	142	13	.	.	PUNCT
ajst-5302	143	1	however	however	ADV
ajst-5302	143	2	,	,	PUNCT
ajst-5302	143	3	according	accord	VERB
ajst-5302	143	4	to	to	ADP
ajst-5302	143	5	the	the	DET
ajst-5302	143	6	detection	detection	NOUN
ajst-5302	143	7	results	result	NOUN
ajst-5302	143	8	,	,	PUNCT
ajst-5302	143	9	this	this	DET
ajst-5302	143	10	method	method	NOUN
ajst-5302	143	11	can	can	AUX
ajst-5302	143	12	well	well	ADV
ajst-5302	143	13	identify	identify	VERB
ajst-5302	143	14	the	the	DET
ajst-5302	143	15	vehicle	vehicle	NOUN
ajst-5302	143	16	information	information	NOUN
ajst-5302	143	17	,	,	PUNCT
ajst-5302	143	18	and	and	CCONJ
ajst-5302	143	19	it	it	PRON
ajst-5302	143	20	can	can	AUX
ajst-5302	143	21	be	be	AUX
ajst-5302	143	22	seen	see	VERB
ajst-5302	143	23	from	from	ADP
ajst-5302	143	24	fig	fig	NOUN
ajst-5302	143	25	.	.	PUNCT
ajst-5302	144	1	12	12	NUM
ajst-5302	144	2	that	that	SCONJ
ajst-5302	144	3	the	the	DET
ajst-5302	144	4	accuracy	accuracy	NOUN
ajst-5302	144	5	rate	rate	NOUN
ajst-5302	144	6	of	of	ADP
ajst-5302	144	7	vehicle	vehicle	NOUN
ajst-5302	144	8	recognition	recognition	NOUN
ajst-5302	144	9	is	be	AUX
ajst-5302	144	10	up	up	ADV
ajst-5302	144	11	to	to	ADP
ajst-5302	144	12	99.43	99.43	NUM
ajst-5302	144	13	%	%	NOUN
ajst-5302	144	14	.	.	PUNCT
ajst-5302	145	1	from	from	ADP
ajst-5302	145	2	the	the	DET
ajst-5302	145	3	point	point	NOUN
ajst-5302	145	4	of	of	ADP
ajst-5302	145	5	view	view	NOUN
ajst-5302	145	6	of	of	ADP
ajst-5302	145	7	probability	probability	NOUN
ajst-5302	145	8	theory	theory	NOUN
ajst-5302	145	9	we	we	PRON
ajst-5302	145	10	can	can	AUX
ajst-5302	145	11	say	say	VERB
ajst-5302	145	12	that	that	SCONJ
ajst-5302	145	13	this	this	PRON
ajst-5302	145	14	is	be	AUX
ajst-5302	145	15	a	a	DET
ajst-5302	145	16	car	car	NOUN
ajst-5302	145	17	.	.	PUNCT
ajst-5302	146	1	the	the	DET
ajst-5302	146	2	above	above	ADJ
ajst-5302	146	3	are	be	AUX
ajst-5302	146	4	the	the	DET
ajst-5302	146	5	detection	detection	NOUN
ajst-5302	146	6	results	result	NOUN
ajst-5302	146	7	of	of	ADP
ajst-5302	146	8	a	a	DET
ajst-5302	146	9	single	single	ADJ
ajst-5302	146	10	target	target	NOUN
ajst-5302	146	11	.	.	PUNCT
ajst-5302	147	1	in	in	ADP
ajst-5302	147	2	order	order	NOUN
ajst-5302	147	3	to	to	PART
ajst-5302	147	4	illustrate	illustrate	VERB
ajst-5302	147	5	the	the	DET
ajst-5302	147	6	correctness	correctness	NOUN
ajst-5302	147	7	of	of	ADP
ajst-5302	147	8	the	the	DET
ajst-5302	147	9	method	method	NOUN
ajst-5302	147	10	,	,	PUNCT
ajst-5302	147	11	the	the	DET
ajst-5302	147	12	detection	detection	NOUN
ajst-5302	147	13	results	result	NOUN
ajst-5302	147	14	of	of	ADP
ajst-5302	147	15	multiple	multiple	ADJ
ajst-5302	147	16	targets	target	NOUN
ajst-5302	147	17	are	be	AUX
ajst-5302	147	18	shown	show	VERB
ajst-5302	147	19	in	in	ADP
ajst-5302	147	20	fig	fig	NOUN
ajst-5302	147	21	.	.	PUNCT
ajst-5302	148	1	13	13	NUM
ajst-5302	148	2	.	.	PUNCT
ajst-5302	149	1	(	(	PUNCT
ajst-5302	149	2	a	a	X
ajst-5302	149	3	)	)	PUNCT
ajst-5302	149	4	original	original	ADJ
ajst-5302	149	5	drawing	drawing	NOUN
ajst-5302	149	6	of	of	ADP
ajst-5302	149	7	vehicle	vehicle	NOUN
ajst-5302	149	8	(	(	PUNCT
ajst-5302	149	9	b	b	NOUN
ajst-5302	149	10	)	)	PUNCT
ajst-5302	149	11	vehicle	vehicle	NOUN
ajst-5302	149	12	test	test	NOUN
ajst-5302	149	13	result	result	NOUN
ajst-5302	149	14	figure	figure	NOUN
ajst-5302	149	15	13	13	NUM
ajst-5302	149	16	.	.	PUNCT
ajst-5302	150	1	comparison	comparison	NOUN
ajst-5302	150	2	of	of	ADP
ajst-5302	150	3	vehicle	vehicle	NOUN
ajst-5302	150	4	detection	detection	NOUN
ajst-5302	150	5	results	result	VERB
ajst-5302	150	6	44	44	NUM
ajst-5302	150	7	fig	fig	NOUN
ajst-5302	150	8	.	.	PUNCT
ajst-5302	151	1	13	13	NUM
ajst-5302	151	2	shows	show	VERB
ajst-5302	151	3	that	that	SCONJ
ajst-5302	151	4	the	the	DET
ajst-5302	151	5	detection	detection	NOUN
ajst-5302	151	6	method	method	NOUN
ajst-5302	151	7	proposed	propose	VERB
ajst-5302	151	8	in	in	ADP
ajst-5302	151	9	this	this	DET
ajst-5302	151	10	paper	paper	NOUN
ajst-5302	151	11	can	can	AUX
ajst-5302	151	12	detect	detect	VERB
ajst-5302	151	13	not	not	PART
ajst-5302	151	14	only	only	ADV
ajst-5302	151	15	a	a	DET
ajst-5302	151	16	single	single	ADJ
ajst-5302	151	17	vehicle	vehicle	NOUN
ajst-5302	151	18	,	,	PUNCT
ajst-5302	151	19	but	but	CCONJ
ajst-5302	151	20	also	also	ADV
ajst-5302	151	21	multiple	multiple	ADJ
ajst-5302	151	22	vehicles	vehicle	NOUN
ajst-5302	151	23	.	.	PUNCT
ajst-5302	152	1	the	the	DET
ajst-5302	152	2	results	result	NOUN
ajst-5302	152	3	are	be	AUX
ajst-5302	152	4	shown	show	VERB
ajst-5302	152	5	in	in	ADP
ajst-5302	152	6	fig	fig	NOUN
ajst-5302	152	7	.	.	PUNCT
ajst-5302	153	1	13(b	13(b	NUM
ajst-5302	153	2	)	)	PUNCT
ajst-5302	153	3	,	,	PUNCT
ajst-5302	153	4	where	where	SCONJ
ajst-5302	153	5	the	the	DET
ajst-5302	153	6	detection	detection	NOUN
ajst-5302	153	7	accuracy	accuracy	NOUN
ajst-5302	153	8	of	of	ADP
ajst-5302	153	9	two	two	NUM
ajst-5302	153	10	vehicles	vehicle	NOUN
ajst-5302	153	11	is	be	AUX
ajst-5302	153	12	99.99	99.99	NUM
ajst-5302	153	13	%	%	NOUN
ajst-5302	153	14	and	and	CCONJ
ajst-5302	153	15	97.73	97.73	NUM
ajst-5302	153	16	%	%	NOUN
ajst-5302	153	17	respectively	respectively	ADV
ajst-5302	153	18	,	,	PUNCT
ajst-5302	153	19	which	which	PRON
ajst-5302	153	20	can	can	AUX
ajst-5302	153	21	also	also	ADV
ajst-5302	153	22	illustrate	illustrate	VERB
ajst-5302	153	23	the	the	DET
ajst-5302	153	24	accuracy	accuracy	NOUN
ajst-5302	153	25	of	of	ADP
ajst-5302	153	26	the	the	DET
ajst-5302	153	27	results	result	NOUN
ajst-5302	153	28	.	.	PUNCT
ajst-5302	154	1	4.2	4.2	NUM
ajst-5302	154	2	.	.	PUNCT
ajst-5302	154	3	dynamic	dynamic	ADJ
ajst-5302	154	4	vehicle	vehicle	NOUN
ajst-5302	154	5	image	image	NOUN
ajst-5302	154	6	recognition	recognition	NOUN
ajst-5302	154	7	for	for	ADP
ajst-5302	154	8	dynamic	dynamic	ADJ
ajst-5302	154	9	vehicle	vehicle	NOUN
ajst-5302	154	10	image	image	NOUN
ajst-5302	154	11	recognition	recognition	NOUN
ajst-5302	154	12	,	,	PUNCT
ajst-5302	154	13	this	this	DET
ajst-5302	154	14	paper	paper	NOUN
ajst-5302	154	15	captures	capture	VERB
ajst-5302	154	16	a	a	DET
ajst-5302	154	17	film	film	NOUN
ajst-5302	154	18	video	video	NOUN
ajst-5302	154	19	and	and	CCONJ
ajst-5302	154	20	identifies	identify	VERB
ajst-5302	154	21	the	the	DET
ajst-5302	154	22	vehicles	vehicle	NOUN
ajst-5302	154	23	in	in	ADP
ajst-5302	154	24	it	it	PRON
ajst-5302	154	25	.	.	PUNCT
ajst-5302	155	1	the	the	DET
ajst-5302	155	2	recognition	recognition	NOUN
ajst-5302	155	3	process	process	NOUN
ajst-5302	155	4	is	be	AUX
ajst-5302	155	5	shown	show	VERB
ajst-5302	155	6	in	in	ADP
ajst-5302	155	7	fig	fig	NOUN
ajst-5302	155	8	.	.	PUNCT
ajst-5302	156	1	14	14	NUM
ajst-5302	156	2	.	.	PUNCT
ajst-5302	157	1	(	(	PUNCT
ajst-5302	157	2	a	a	X
ajst-5302	157	3	)	)	PUNCT
ajst-5302	157	4	dynamic	dynamic	ADJ
ajst-5302	157	5	vehicle	vehicle	NOUN
ajst-5302	157	6	identification	identification	NOUN
ajst-5302	157	7	i	i	PRON
ajst-5302	157	8	(	(	PUNCT
ajst-5302	157	9	b	b	NOUN
ajst-5302	157	10	)	)	PUNCT
ajst-5302	157	11	dynamic	dynamic	ADJ
ajst-5302	157	12	vehicle	vehicle	NOUN
ajst-5302	157	13	identification	identification	NOUN
ajst-5302	157	14	ii	ii	NOUN
ajst-5302	157	15	(	(	PUNCT
ajst-5302	157	16	c	c	NOUN
ajst-5302	157	17	)	)	PUNCT
ajst-5302	157	18	dynamic	dynamic	ADJ
ajst-5302	157	19	vehicle	vehicle	NOUN
ajst-5302	157	20	identification	identification	NOUN
ajst-5302	157	21	iii	iii	X
ajst-5302	157	22	(	(	PUNCT
ajst-5302	157	23	d	d	NOUN
ajst-5302	157	24	)	)	PUNCT
ajst-5302	157	25	dynamic	dynamic	ADJ
ajst-5302	157	26	vehicle	vehicle	NOUN
ajst-5302	157	27	identification	identification	NOUN
ajst-5302	157	28	iv	iv	ADP
ajst-5302	157	29	(	(	PUNCT
ajst-5302	157	30	e	e	NOUN
ajst-5302	157	31	)	)	PUNCT
ajst-5302	157	32	dynamic	dynamic	ADJ
ajst-5302	157	33	vehicle	vehicle	NOUN
ajst-5302	157	34	identification	identification	NOUN
ajst-5302	157	35	v	v	NOUN
ajst-5302	157	36	(	(	PUNCT
ajst-5302	157	37	f	f	X
ajst-5302	157	38	)	)	PUNCT
ajst-5302	157	39	dynamic	dynamic	ADJ
ajst-5302	157	40	vehicle	vehicle	NOUN
ajst-5302	157	41	identification	identification	NOUN
ajst-5302	157	42	vi	vi	NOUN
ajst-5302	157	43	figure	figure	NOUN
ajst-5302	157	44	14	14	NUM
ajst-5302	157	45	.	.	PUNCT
ajst-5302	158	1	dynamic	dynamic	ADJ
ajst-5302	158	2	vehicle	vehicle	NOUN
ajst-5302	158	3	identification	identification	NOUN
ajst-5302	158	4	diagram	diagram	NOUN
ajst-5302	158	5	fig	fig	NOUN
ajst-5302	158	6	.	.	PUNCT
ajst-5302	159	1	14	14	NUM
ajst-5302	159	2	shows	show	VERB
ajst-5302	159	3	the	the	DET
ajst-5302	159	4	vehicle	vehicle	NOUN
ajst-5302	159	5	recognition	recognition	NOUN
ajst-5302	159	6	process	process	NOUN
ajst-5302	159	7	in	in	ADP
ajst-5302	159	8	the	the	DET
ajst-5302	159	9	film	film	NOUN
ajst-5302	159	10	material	material	NOUN
ajst-5302	159	11	provided	provide	VERB
ajst-5302	159	12	in	in	ADP
ajst-5302	159	13	this	this	DET
ajst-5302	159	14	paper	paper	NOUN
ajst-5302	159	15	.	.	PUNCT
ajst-5302	160	1	the	the	DET
ajst-5302	160	2	six	six	NUM
ajst-5302	160	3	subgraphs	subgraph	NOUN
ajst-5302	160	4	show	show	VERB
ajst-5302	160	5	the	the	DET
ajst-5302	160	6	appearance	appearance	NOUN
ajst-5302	160	7	of	of	ADP
ajst-5302	160	8	cars	car	NOUN
ajst-5302	160	9	as	as	ADP
ajst-5302	160	10	the	the	DET
ajst-5302	160	11	result	result	NOUN
ajst-5302	160	12	of	of	ADP
ajst-5302	160	13	recognition	recognition	NOUN
ajst-5302	160	14	respectively	respectively	ADV
ajst-5302	160	15	.	.	PUNCT
ajst-5302	161	1	it	it	PRON
ajst-5302	161	2	can	can	AUX
ajst-5302	161	3	be	be	AUX
ajst-5302	161	4	seen	see	VERB
ajst-5302	161	5	that	that	SCONJ
ajst-5302	161	6	the	the	DET
ajst-5302	161	7	algorithm	algorithm	NOUN
ajst-5302	161	8	provided	provide	VERB
ajst-5302	161	9	in	in	ADP
ajst-5302	161	10	this	this	DET
ajst-5302	161	11	paper	paper	NOUN
ajst-5302	161	12	can	can	AUX
ajst-5302	161	13	also	also	ADV
ajst-5302	161	14	better	well	ADV
ajst-5302	161	15	realize	realize	VERB
ajst-5302	161	16	the	the	DET
ajst-5302	161	17	recognition	recognition	NOUN
ajst-5302	161	18	of	of	ADP
ajst-5302	161	19	the	the	DET
ajst-5302	161	20	whole	whole	ADJ
ajst-5302	161	21	vehicle	vehicle	NOUN
ajst-5302	161	22	in	in	ADP
ajst-5302	161	23	the	the	DET
ajst-5302	161	24	process	process	NOUN
ajst-5302	161	25	of	of	ADP
ajst-5302	161	26	vehicle	vehicle	NOUN
ajst-5302	161	27	movement	movement	NOUN
ajst-5302	161	28	.	.	PUNCT
ajst-5302	162	1	the	the	DET
ajst-5302	162	2	figure	figure	NOUN
ajst-5302	162	3	not	not	PART
ajst-5302	162	4	only	only	ADV
ajst-5302	162	5	contains	contain	VERB
ajst-5302	162	6	the	the	DET
ajst-5302	162	7	recognition	recognition	NOUN
ajst-5302	162	8	of	of	ADP
ajst-5302	162	9	a	a	DET
ajst-5302	162	10	single	single	ADJ
ajst-5302	162	11	vehicle	vehicle	NOUN
ajst-5302	162	12	,	,	PUNCT
ajst-5302	162	13	but	but	CCONJ
ajst-5302	162	14	also	also	ADV
ajst-5302	162	15	includes	include	VERB
ajst-5302	162	16	the	the	DET
ajst-5302	162	17	recognition	recognition	NOUN
ajst-5302	162	18	of	of	ADP
ajst-5302	162	19	multiple	multiple	ADJ
ajst-5302	162	20	vehicles	vehicle	NOUN
ajst-5302	162	21	.	.	PUNCT
ajst-5302	163	1	the	the	DET
ajst-5302	163	2	figure	figure	NOUN
ajst-5302	163	3	also	also	ADV
ajst-5302	163	4	includes	include	VERB
ajst-5302	163	5	the	the	DET
ajst-5302	163	6	recognition	recognition	NOUN
ajst-5302	163	7	of	of	ADP
ajst-5302	163	8	vehicles	vehicle	NOUN
ajst-5302	163	9	in	in	ADP
ajst-5302	163	10	good	good	ADJ
ajst-5302	163	11	light	light	NOUN
ajst-5302	163	12	and	and	CCONJ
ajst-5302	163	13	dim	dim	ADJ
ajst-5302	163	14	light	light	NOUN
ajst-5302	163	15	.	.	PUNCT
ajst-5302	164	1	no	no	ADV
ajst-5302	164	2	matter	matter	ADV
ajst-5302	164	3	what	what	DET
ajst-5302	164	4	kind	kind	NOUN
ajst-5302	164	5	of	of	ADP
ajst-5302	164	6	environment	environment	NOUN
ajst-5302	164	7	,	,	PUNCT
ajst-5302	164	8	the	the	DET
ajst-5302	164	9	recognition	recognition	NOUN
ajst-5302	164	10	method	method	NOUN
ajst-5302	164	11	in	in	ADP
ajst-5302	164	12	this	this	DET
ajst-5302	164	13	paper	paper	NOUN
ajst-5302	164	14	can	can	AUX
ajst-5302	164	15	better	well	ADV
ajst-5302	164	16	identify	identify	VERB
ajst-5302	164	17	the	the	DET
ajst-5302	164	18	dynamic	dynamic	ADJ
ajst-5302	164	19	process	process	NOUN
ajst-5302	164	20	of	of	ADP
ajst-5302	164	21	vehicles	vehicle	NOUN
ajst-5302	164	22	.	.	PUNCT
ajst-5302	165	1	5	5	X
ajst-5302	165	2	.	.	X
ajst-5302	165	3	conclusion	conclusion	NOUN
ajst-5302	165	4	this	this	DET
ajst-5302	165	5	paper	paper	NOUN
ajst-5302	165	6	takes	take	VERB
ajst-5302	165	7	vehicle	vehicle	NOUN
ajst-5302	165	8	recognition	recognition	NOUN
ajst-5302	165	9	as	as	ADP
ajst-5302	165	10	the	the	DET
ajst-5302	165	11	research	research	NOUN
ajst-5302	165	12	objective	objective	NOUN
ajst-5302	165	13	,	,	PUNCT
ajst-5302	165	14	and	and	CCONJ
ajst-5302	165	15	studies	study	NOUN
ajst-5302	165	16	the	the	DET
ajst-5302	165	17	application	application	NOUN
ajst-5302	165	18	of	of	ADP
ajst-5302	165	19	deep	deep	ADJ
ajst-5302	165	20	learning	learning	NOUN
ajst-5302	165	21	in	in	ADP
ajst-5302	165	22	image	image	NOUN
ajst-5302	165	23	processing	processing	NOUN
ajst-5302	165	24	,	,	PUNCT
ajst-5302	165	25	which	which	PRON
ajst-5302	165	26	includes	include	VERB
ajst-5302	165	27	image	image	NOUN
ajst-5302	165	28	information	information	NOUN
ajst-5302	165	29	acquisition	acquisition	NOUN
ajst-5302	165	30	,	,	PUNCT
ajst-5302	165	31	vehicle	vehicle	NOUN
ajst-5302	165	32	image	image	NOUN
ajst-5302	165	33	preprocessing	preprocessing	NOUN
ajst-5302	165	34	,	,	PUNCT
ajst-5302	165	35	vehicle	vehicle	NOUN
ajst-5302	165	36	image	image	NOUN
ajst-5302	165	37	segmentation	segmentation	NOUN
ajst-5302	165	38	,	,	PUNCT
ajst-5302	165	39	vehicle	vehicle	NOUN
ajst-5302	165	40	image	image	NOUN
ajst-5302	165	41	feature	feature	NOUN
ajst-5302	165	42	extraction	extraction	NOUN
ajst-5302	165	43	and	and	CCONJ
ajst-5302	165	44	vehicle	vehicle	NOUN
ajst-5302	165	45	recognition	recognition	NOUN
ajst-5302	165	46	several	several	ADJ
ajst-5302	165	47	processes	process	NOUN
ajst-5302	165	48	.	.	PUNCT
ajst-5302	166	1	in	in	ADP
ajst-5302	166	2	the	the	DET
ajst-5302	166	3	research	research	NOUN
ajst-5302	166	4	process	process	NOUN
ajst-5302	166	5	,	,	PUNCT
ajst-5302	166	6	the	the	DET
ajst-5302	166	7	deep	deep	ADJ
ajst-5302	166	8	learning	learning	NOUN
ajst-5302	166	9	method	method	NOUN
ajst-5302	166	10	of	of	ADP
ajst-5302	166	11	convolutional	convolutional	ADJ
ajst-5302	166	12	neural	neural	ADJ
ajst-5302	166	13	network	network	NOUN
ajst-5302	166	14	is	be	AUX
ajst-5302	166	15	studied	study	VERB
ajst-5302	166	16	,	,	PUNCT
ajst-5302	166	17	and	and	CCONJ
ajst-5302	166	18	the	the	DET
ajst-5302	166	19	ssd	ssd	NOUN
ajst-5302	166	20	algorithm	algorithm	NOUN
ajst-5302	166	21	based	base	VERB
ajst-5302	166	22	on	on	ADP
ajst-5302	166	23	convolutional	convolutional	ADJ
ajst-5302	166	24	neural	neural	ADJ
ajst-5302	166	25	network	network	NOUN
ajst-5302	166	26	is	be	AUX
ajst-5302	166	27	used	use	VERB
ajst-5302	166	28	to	to	PART
ajst-5302	166	29	realize	realize	VERB
ajst-5302	166	30	the	the	DET
ajst-5302	166	31	recognition	recognition	NOUN
ajst-5302	166	32	process	process	NOUN
ajst-5302	166	33	of	of	ADP
ajst-5302	166	34	vehicle	vehicle	NOUN
ajst-5302	166	35	image	image	NOUN
ajst-5302	166	36	.	.	PUNCT
ajst-5302	167	1	in	in	ADP
ajst-5302	167	2	the	the	DET
ajst-5302	167	3	specific	specific	ADJ
ajst-5302	167	4	process	process	NOUN
ajst-5302	167	5	of	of	ADP
ajst-5302	167	6	identification	identification	NOUN
ajst-5302	167	7	,	,	PUNCT
ajst-5302	167	8	opencv	opencv	PROPN
ajst-5302	167	9	,	,	PUNCT
ajst-5302	167	10	an	an	DET
ajst-5302	167	11	open	open	ADJ
ajst-5302	167	12	source	source	NOUN
ajst-5302	167	13	tool	tool	PROPN
ajst-5302	167	14	box	box	PROPN
ajst-5302	167	15	,	,	PUNCT
ajst-5302	167	16	is	be	AUX
ajst-5302	167	17	used	use	VERB
ajst-5302	167	18	to	to	PART
ajst-5302	167	19	detect	detect	VERB
ajst-5302	167	20	the	the	DET
ajst-5302	167	21	target	target	NOUN
ajst-5302	167	22	object	object	NOUN
ajst-5302	167	23	,	,	PUNCT
ajst-5302	167	24	and	and	CCONJ
ajst-5302	167	25	the	the	DET
ajst-5302	167	26	detection	detection	NOUN
ajst-5302	167	27	of	of	ADP
ajst-5302	167	28	stationary	stationary	ADJ
ajst-5302	167	29	state	state	NOUN
ajst-5302	167	30	and	and	CCONJ
ajst-5302	167	31	moving	move	VERB
ajst-5302	167	32	state	state	NOUN
ajst-5302	167	33	of	of	ADP
ajst-5302	167	34	the	the	DET
ajst-5302	167	35	vehicle	vehicle	NOUN
ajst-5302	167	36	is	be	AUX
ajst-5302	167	37	realized	realize	VERB
ajst-5302	167	38	.	.	PUNCT
ajst-5302	168	1	references	reference	NOUN
ajst-5302	168	2	[	[	X
ajst-5302	168	3	1	1	NUM
ajst-5302	168	4	]	]	X
ajst-5302	168	5	hinton	hinton	PROPN
ajst-5302	168	6	g	g	PROPN
ajst-5302	168	7	e	e	PROPN
ajst-5302	168	8	,	,	PUNCT
ajst-5302	168	9	osindero	osindero	NOUN
ajst-5302	168	10	s	s	PART
ajst-5302	168	11	,	,	PUNCT
ajst-5302	168	12	teh	teh	PROPN
ajst-5302	168	13	y	y	PROPN
ajst-5302	168	14	w.	w.	PROPN
ajst-5302	169	1	a	a	DET
ajst-5302	169	2	fast	fast	ADJ
ajst-5302	169	3	learning	learn	VERB
ajst-5302	169	4	algorithm	algorithm	NOUN
ajst-5302	169	5	for	for	ADP
ajst-5302	169	6	deep	deep	ADJ
ajst-5302	169	7	belief	belief	NOUN
ajst-5302	169	8	nets[j	nets[j	ADP
ajst-5302	169	9	]	]	PUNCT
ajst-5302	169	10	.	.	PUNCT
ajst-5302	170	1	neural	neural	ADJ
ajst-5302	170	2	computation	computation	NOUN
ajst-5302	170	3	,	,	PUNCT
ajst-5302	170	4	2006	2006	NUM
ajst-5302	170	5	,	,	PUNCT
ajst-5302	170	6	18(7	18(7	NUM
ajst-5302	170	7	):	):	PUNCT
ajst-5302	170	8	15271554	15271554	NUM
ajst-5302	170	9	.	.	PUNCT
ajst-5302	171	1	[	[	X
ajst-5302	171	2	2	2	X
ajst-5302	171	3	]	]	PUNCT
ajst-5302	171	4	greenspan	greenspan	PROPN
ajst-5302	171	5	h	h	PROPN
ajst-5302	171	6	,	,	PUNCT
ajst-5302	171	7	van	van	PROPN
ajst-5302	171	8	ginneken	ginneken	PROPN
ajst-5302	171	9	b	b	PROPN
ajst-5302	171	10	,	,	PUNCT
ajst-5302	171	11	summers	summer	NOUN
ajst-5302	171	12	r	r	NOUN
ajst-5302	171	13	m.	m.	NOUN
ajst-5302	171	14	guest	guest	NOUN
ajst-5302	171	15	editorial	editorial	NOUN
ajst-5302	171	16	deep	deep	ADJ
ajst-5302	171	17	learning	learning	NOUN
ajst-5302	171	18	in	in	ADP
ajst-5302	171	19	medical	medical	ADJ
ajst-5302	171	20	imaging	imaging	NOUN
ajst-5302	171	21	:	:	PUNCT
ajst-5302	171	22	overview	overview	NOUN
ajst-5302	171	23	and	and	CCONJ
ajst-5302	171	24	future	future	ADJ
ajst-5302	171	25	promise	promise	NOUN
ajst-5302	171	26	of	of	ADP
ajst-5302	171	27	an	an	DET
ajst-5302	171	28	exciting	exciting	ADJ
ajst-5302	171	29	new	new	ADJ
ajst-5302	171	30	technique[j	technique[j	PROPN
ajst-5302	171	31	]	]	PUNCT
ajst-5302	171	32	.	.	PUNCT
ajst-5302	172	1	ieee	ieee	NOUN
ajst-5302	172	2	transactions	transaction	NOUN
ajst-5302	172	3	on	on	ADP
ajst-5302	172	4	medical	medical	ADJ
ajst-5302	172	5	imaging	imaging	NOUN
ajst-5302	172	6	,	,	PUNCT
ajst-5302	172	7	2016	2016	NUM
ajst-5302	172	8	,	,	PUNCT
ajst-5302	172	9	35(5	35(5	NUM
ajst-5302	172	10	):	):	PUNCT
ajst-5302	172	11	1153	1153	NUM
ajst-5302	172	12	-	-	SYM
ajst-5302	172	13	1159	1159	NUM
ajst-5302	172	14	.	.	PUNCT
ajst-5302	173	1	[	[	X
ajst-5302	173	2	3	3	X
ajst-5302	173	3	]	]	X
ajst-5302	173	4	amodei	amodei	NOUN
ajst-5302	173	5	d	d	NOUN
ajst-5302	173	6	,	,	PUNCT
ajst-5302	173	7	ananthanarayanan	ananthanarayanan	PROPN
ajst-5302	173	8	s	s	PROPN
ajst-5302	173	9	,	,	PUNCT
ajst-5302	173	10	anubhai	anubhai	PROPN
ajst-5302	173	11	r	r	PROPN
ajst-5302	173	12	,	,	PUNCT
ajst-5302	173	13	et	et	PROPN
ajst-5302	173	14	al	al	PROPN
ajst-5302	173	15	.	.	PUNCT
ajst-5302	174	1	deep	deep	ADJ
ajst-5302	174	2	speech	speech	NOUN
ajst-5302	174	3	2	2	NUM
ajst-5302	174	4	:	:	PUNCT
ajst-5302	174	5	end	end	NOUN
ajst-5302	174	6	-	-	PUNCT
ajst-5302	174	7	to	to	ADP
ajst-5302	174	8	-	-	PUNCT
ajst-5302	174	9	end	end	NOUN
ajst-5302	174	10	speech	speech	NOUN
ajst-5302	174	11	recognition	recognition	NOUN
ajst-5302	174	12	in	in	ADP
ajst-5302	174	13	english	english	PROPN
ajst-5302	174	14	and	and	CCONJ
ajst-5302	174	15	mandarin[c	mandarin[c	NOUN
ajst-5302	174	16	]	]	PUNCT
ajst-5302	174	17	.	.	PUNCT
ajst-5302	175	1	international	international	ADJ
ajst-5302	175	2	conference	conference	NOUN
ajst-5302	175	3	on	on	ADP
ajst-5302	175	4	machine	machine	NOUN
ajst-5302	175	5	learning	learning	NOUN
ajst-5302	175	6	.	.	PUNCT
ajst-5302	176	1	2016	2016	NUM
ajst-5302	176	2	:	:	PUNCT
ajst-5302	176	3	173	173	NUM
ajst-5302	176	4	-	-	SYM
ajst-5302	176	5	182	182	NUM
ajst-5302	176	6	.	.	PUNCT
ajst-5302	177	1	[	[	X
ajst-5302	177	2	4	4	X
ajst-5302	177	3	]	]	X
ajst-5302	177	4	collobert	collobert	PROPN
ajst-5302	177	5	r	r	PROPN
ajst-5302	177	6	,	,	PUNCT
ajst-5302	177	7	weston	weston	PROPN
ajst-5302	177	8	j.	j.	PROPN
ajst-5302	178	1	a	a	DET
ajst-5302	178	2	unified	unified	ADJ
ajst-5302	178	3	architecture	architecture	NOUN
ajst-5302	178	4	for	for	ADP
ajst-5302	178	5	natural	natural	ADJ
ajst-5302	178	6	language	language	NOUN
ajst-5302	178	7	processing	processing	NOUN
ajst-5302	178	8	:	:	PUNCT
ajst-5302	178	9	deep	deep	ADJ
ajst-5302	178	10	neural	neural	ADJ
ajst-5302	178	11	networks	network	NOUN
ajst-5302	178	12	with	with	ADP
ajst-5302	178	13	multitask	multitask	ADJ
ajst-5302	178	14	learning[c	learning[c	PROPN
ajst-5302	178	15	]	]	PUNCT
ajst-5302	178	16	.	.	PUNCT
ajst-5302	179	1	proceedings	proceeding	NOUN
ajst-5302	179	2	of	of	ADP
ajst-5302	179	3	the	the	DET
ajst-5302	179	4	25th	25th	ADJ
ajst-5302	179	5	international	international	ADJ
ajst-5302	179	6	conference	conference	NOUN
ajst-5302	179	7	on	on	ADP
ajst-5302	179	8	machine	machine	NOUN
ajst-5302	179	9	learning	learning	NOUN
ajst-5302	179	10	.	.	PUNCT
ajst-5302	180	1	acm	acm	PROPN
ajst-5302	180	2	,	,	PUNCT
ajst-5302	180	3	2008	2008	NUM
ajst-5302	180	4	:	:	PUNCT
ajst-5302	180	5	160	160	NUM
ajst-5302	180	6	-	-	SYM
ajst-5302	180	7	167	167	NUM
ajst-5302	180	8	.	.	PUNCT
ajst-5302	181	1	[	[	X
ajst-5302	181	2	5	5	X
ajst-5302	181	3	]	]	PUNCT
ajst-5302	181	4	ding	de	VERB
ajst-5302	181	5	hong	hong	PROPN
ajst-5302	181	6	,	,	PUNCT
ajst-5302	181	7	rao	rao	PROPN
ajst-5302	181	8	wanxian	wanxian	PROPN
ajst-5302	181	9	.	.	PUNCT
ajst-5302	182	1	design	design	NOUN
ajst-5302	182	2	of	of	ADP
ajst-5302	182	3	human	human	ADJ
ajst-5302	182	4	behavior	behavior	NOUN
ajst-5302	182	5	detection	detection	NOUN
ajst-5302	182	6	system	system	NOUN
ajst-5302	182	7	based	base	VERB
ajst-5302	182	8	on	on	ADP
ajst-5302	182	9	deep	deep	ADJ
ajst-5302	182	10	learning	learning	NOUN
ajst-5302	182	11	[	[	X
ajst-5302	182	12	j	j	X
ajst-5302	182	13	]	]	X
ajst-5302	182	14	.	.	PUNCT
ajst-5302	183	1	electronic	electronic	ADJ
ajst-5302	183	2	technology	technology	NOUN
ajst-5302	183	3	and	and	CCONJ
ajst-5302	183	4	software	software	NOUN
ajst-5302	183	5	engineering,2019,no.155(09):79	engineering,2019,no.155(09):79	NOUN
ajst-5302	183	6	-	-	SYM
ajst-5302	183	7	80	80	NUM
ajst-5302	183	8	.	.	PUNCT
ajst-5302	183	9	45	45	NUM
ajst-5302	184	1	[	[	SYM
ajst-5302	184	2	6	6	NUM
ajst-5302	184	3	]	]	X
ajst-5302	184	4	gao	gao	PROPN
ajst-5302	184	5	yuan	yuan	PROPN
ajst-5302	184	6	,	,	PUNCT
ajst-5302	184	7	wang	wang	PROPN
ajst-5302	184	8	xiaochen	xiaochen	PROPN
ajst-5302	184	9	,	,	PUNCT
ajst-5302	184	10	qin	qin	PROPN
ajst-5302	184	11	pinle	pinle	PROPN
ajst-5302	184	12	,	,	PUNCT
ajst-5302	184	13	et	et	PROPN
ajst-5302	184	14	al	al	PROPN
ajst-5302	184	15	.	.	PUNCT
ajst-5302	185	1	superresolution	superresolution	NOUN
ajst-5302	185	2	reconstruction	reconstruction	NOUN
ajst-5302	185	3	of	of	ADP
ajst-5302	185	4	medical	medical	ADJ
ajst-5302	185	5	image	image	NOUN
ajst-5302	185	6	based	base	VERB
ajst-5302	185	7	on	on	ADP
ajst-5302	185	8	depth	depth	NOUN
ajst-5302	185	9	-	-	PUNCT
ajst-5302	185	10	separable	separable	NOUN
ajst-5302	185	11	convolution	convolution	NOUN
ajst-5302	185	12	and	and	CCONJ
ajst-5302	185	13	wide	wide	ADJ
ajst-5302	185	14	residual	residual	ADJ
ajst-5302	185	15	network	network	NOUN
ajst-5302	185	16	[	[	X
ajst-5302	185	17	j	j	X
ajst-5302	185	18	]	]	X
ajst-5302	185	19	.	.	PUNCT
ajst-5302	186	1	journal	journal	PROPN
ajst-5302	186	2	of	of	ADP
ajst-5302	186	3	computer	computer	NOUN
ajst-5302	186	4	applications,2019,39(09):2731	applications,2019,39(09):2731	NOUN
ajst-5302	186	5	-	-	PUNCT
ajst-5302	186	6	2737	2737	NUM
ajst-5302	186	7	.	.	PUNCT
ajst-5302	187	1	[	[	X
ajst-5302	187	2	7	7	X
ajst-5302	187	3	]	]	X
ajst-5302	187	4	jibbe	jibbe	NOUN
ajst-5302	187	5	m	m	PROPN
ajst-5302	187	6	k	k	PROPN
ajst-5302	187	7	,	,	PUNCT
ajst-5302	187	8	kannan	kannan	PROPN
ajst-5302	187	9	s.	s.	PROPN
ajst-5302	187	10	method	method	VERB
ajst-5302	187	11	to	to	PART
ajst-5302	187	12	handle	handle	VERB
ajst-5302	187	13	demand	demand	NOUN
ajst-5302	187	14	based	base	VERB
ajst-5302	187	15	dynamic	dynamic	ADJ
ajst-5302	187	16	cache	cache	NOUN
ajst-5302	187	17	allocation	allocation	NOUN
ajst-5302	187	18	between	between	ADP
ajst-5302	187	19	ssd	ssd	NOUN
ajst-5302	187	20	and	and	CCONJ
ajst-5302	187	21	raid	raid	NOUN
ajst-5302	187	22	cache	cache	PROPN
ajst-5302	187	23	:	:	PUNCT
ajst-5302	187	24	u.s	u.s	PROPN
ajst-5302	187	25	.	.	PROPN
ajst-5302	187	26	patent	patent	NOUN
ajst-5302	187	27	application	application	NOUN
ajst-5302	187	28	12/070,531[p	12/070,531[p	NUM
ajst-5302	187	29	]	]	PUNCT
ajst-5302	187	30	.	.	PUNCT
ajst-5302	188	1	2009	2009	NUM
ajst-5302	188	2	-	-	SYM
ajst-5302	188	3	8	8	NUM
ajst-5302	188	4	-	-	SYM
ajst-5302	188	5	20	20	NUM
ajst-5302	188	6	.	.	PUNCT
ajst-5302	189	1	[	[	X
ajst-5302	189	2	8	8	NUM
ajst-5302	189	3	]	]	X
ajst-5302	189	4	liu	liu	PROPN
ajst-5302	189	5	w	w	PROPN
ajst-5302	189	6	,	,	PUNCT
ajst-5302	189	7	anguelov	anguelov	NOUN
ajst-5302	189	8	d	d	NOUN
ajst-5302	189	9	,	,	PUNCT
ajst-5302	189	10	erhan	erhan	ADP
ajst-5302	189	11	d	d	PROPN
ajst-5302	189	12	,	,	PUNCT
ajst-5302	189	13	et	et	PROPN
ajst-5302	189	14	al	al	PROPN
ajst-5302	189	15	.	.	PROPN
ajst-5302	189	16	ssd	ssd	PROPN
ajst-5302	189	17	:	:	PUNCT
ajst-5302	189	18	single	single	ADJ
ajst-5302	189	19	shot	shot	NOUN
ajst-5302	189	20	multibox	multibox	PROPN
ajst-5302	189	21	detector[c	detector[c	PROPN
ajst-5302	189	22	]	]	PUNCT
ajst-5302	189	23	.	.	PUNCT
ajst-5302	190	1	european	european	ADJ
ajst-5302	190	2	conference	conference	PROPN
ajst-5302	190	3	on	on	ADP
ajst-5302	190	4	computer	computer	NOUN
ajst-5302	190	5	vision	vision	NOUN
ajst-5302	190	6	.	.	PUNCT
ajst-5302	191	1	springer	springer	NOUN
ajst-5302	191	2	,	,	PUNCT
ajst-5302	191	3	cham	cham	PROPN
ajst-5302	191	4	,	,	PUNCT
ajst-5302	191	5	2016	2016	NUM
ajst-5302	191	6	:	:	PUNCT
ajst-5302	191	7	21	21	NUM
ajst-5302	191	8	-	-	SYM
ajst-5302	191	9	37	37	NUM
ajst-5302	191	10	.	.	PUNCT
ajst-5302	192	1	[	[	X
ajst-5302	192	2	9	9	NUM
ajst-5302	192	3	]	]	X
ajst-5302	192	4	qin	qin	NOUN
ajst-5302	192	5	xiaowen	xiaowen	PROPN
ajst-5302	192	6	,	,	PUNCT
ajst-5302	192	7	wen	wen	PROPN
ajst-5302	192	8	zhifang	zhifang	PROPN
ajst-5302	192	9	,	,	PUNCT
ajst-5302	192	10	qiao	qiao	PROPN
ajst-5302	192	11	weiwei	weiwei	PROPN
ajst-5302	192	12	.	.	PUNCT
ajst-5302	193	1	image	image	NOUN
ajst-5302	193	2	processing	processing	NOUN
ajst-5302	193	3	based	base	VERB
ajst-5302	193	4	on	on	ADP
ajst-5302	193	5	opencv	opencv	PROPN
ajst-5302	194	1	[	[	X
ajst-5302	194	2	j	j	X
ajst-5302	194	3	]	]	X
ajst-5302	194	4	.	.	PUNCT
ajst-5302	195	1	electronic	electronic	ADJ
ajst-5302	195	2	testing	testing	NOUN
ajst-5302	195	3	and	and	CCONJ
ajst-5302	195	4	testing,2011,no.229(07):39	testing,2011,no.229(07):39	NUM
ajst-5302	195	5	-	-	PUNCT
ajst-5302	195	6	41	41	NUM
ajst-5302	195	7	.	.	PUNCT
ajst-5302	196	1	[	[	X
ajst-5302	196	2	10	10	NUM
ajst-5302	196	3	]	]	X
ajst-5302	196	4	qian	qian	PROPN
ajst-5302	196	5	y	y	PROPN
ajst-5302	196	6	,	,	PUNCT
ajst-5302	196	7	fan	fan	PROPN
ajst-5302	196	8	y	y	PROPN
ajst-5302	196	9	,	,	PUNCT
ajst-5302	196	10	hu	hu	PROPN
ajst-5302	196	11	w	w	PROPN
ajst-5302	196	12	,	,	PUNCT
ajst-5302	196	13	et	et	PROPN
ajst-5302	196	14	al	al	PROPN
ajst-5302	196	15	.	.	PROPN
ajst-5302	197	1	on	on	ADP
ajst-5302	197	2	the	the	DET
ajst-5302	197	3	training	training	NOUN
ajst-5302	197	4	aspects	aspect	NOUN
ajst-5302	197	5	of	of	ADP
ajst-5302	197	6	deep	deep	ADJ
ajst-5302	197	7	neural	neural	ADJ
ajst-5302	197	8	network	network	NOUN
ajst-5302	197	9	(	(	PUNCT
ajst-5302	197	10	dnn	dnn	PROPN
ajst-5302	197	11	)	)	PUNCT
ajst-5302	197	12	for	for	ADP
ajst-5302	197	13	parametric	parametric	ADJ
ajst-5302	197	14	tts	tts	PROPN
ajst-5302	197	15	synthesis[c	synthesis[c	PROPN
ajst-5302	197	16	]	]	PUNCT
ajst-5302	197	17	.	.	PUNCT
ajst-5302	197	18	2014	2014	NUM
ajst-5302	197	19	ieee	ieee	PROPN
ajst-5302	197	20	international	international	ADJ
ajst-5302	197	21	conference	conference	NOUN
ajst-5302	197	22	on	on	ADP
ajst-5302	197	23	acoustics	acoustic	NOUN
ajst-5302	197	24	,	,	PUNCT
ajst-5302	197	25	speech	speech	NOUN
ajst-5302	197	26	and	and	CCONJ
ajst-5302	197	27	signal	signal	NOUN
ajst-5302	197	28	processing	processing	NOUN
ajst-5302	197	29	(	(	PUNCT
ajst-5302	197	30	icassp	icassp	PROPN
ajst-5302	197	31	)	)	PUNCT
ajst-5302	197	32	.	.	PUNCT
ajst-5302	198	1	ieee	ieee	PROPN
ajst-5302	198	2	,	,	PUNCT
ajst-5302	198	3	2014	2014	NUM
ajst-5302	198	4	:	:	PUNCT
ajst-5302	198	5	3829	3829	NUM
ajst-5302	198	6	-	-	SYM
ajst-5302	198	7	3833	3833	NUM
ajst-5302	198	8	.	.	PUNCT
ajst-5302	199	1	[	[	X
ajst-5302	199	2	11	11	NUM
ajst-5302	199	3	]	]	X
ajst-5302	199	4	lu	lu	PROPN
ajst-5302	199	5	hongtao	hongtao	PROPN
ajst-5302	199	6	,	,	PUNCT
ajst-5302	199	7	zhang	zhang	PROPN
ajst-5302	199	8	qinchuan	qinchuan	PROPN
ajst-5302	199	9	.	.	PUNCT
ajst-5302	200	1	application	application	NOUN
ajst-5302	200	2	of	of	ADP
ajst-5302	200	3	deep	deep	ADJ
ajst-5302	200	4	convolutional	convolutional	ADJ
ajst-5302	200	5	neural	neural	ADJ
ajst-5302	200	6	networks	network	NOUN
ajst-5302	200	7	in	in	ADP
ajst-5302	200	8	computer	computer	NOUN
ajst-5302	200	9	vision	vision	NOUN
ajst-5302	201	1	[	[	X
ajst-5302	201	2	j	j	X
ajst-5302	201	3	]	]	X
ajst-5302	201	4	.	.	PUNCT
ajst-5302	202	1	data	datum	NOUN
ajst-5302	202	2	acquisition	acquisition	NOUN
ajst-5302	202	3	and	and	CCONJ
ajst-5302	202	4	processing,2016,31(01):1	processing,2016,31(01):1	PROPN
ajst-5302	202	5	-	-	PUNCT
ajst-5302	202	6	17	17	NUM
ajst-5302	202	7	.	.	PUNCT