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1.
I
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s
s
in
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[
1
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–
[
3
]
.
So
lu
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ased
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co
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4
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,
[
5
]
.
T
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tr
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to
im
p
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f
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in
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s
[
6
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,
[
7
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.
W
id
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ap
p
licab
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f
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s
in
clu
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ed
ical
an
d
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ca
r
e
[
8
]
,
in
d
u
s
tr
y
[
9
]
,
ag
r
ic
u
ltu
r
e
[
1
0
]
,
[
1
1
]
,
tr
an
s
p
o
r
tatio
n
[
1
2
]
,
[
1
3
]
,
s
ea
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ch
an
d
r
escu
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[
1
4
]
,
d
is
aster
r
esp
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n
s
e
[
1
5
]
,
an
d
e
n
v
ir
o
n
m
en
tal
r
e
s
ea
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ch
[
1
6
]
.
M
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[
1
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F
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C
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.
[
1
7
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Y
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I
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[
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9
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.
T
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ased
v
e
h
icle
d
etec
tio
n
an
d
tr
ac
k
in
g
s
y
s
tem
f
o
r
u
r
b
a
n
in
tellig
en
t
tr
a
n
s
p
o
r
tatio
n
s
y
s
tem
s
(
I
T
S)
a
p
p
li
ca
tio
n
s
.
T
h
e
m
o
d
els
wer
e
ev
alu
ated
u
n
d
er
th
e
s
am
e
ex
p
er
im
en
tal
co
n
d
itio
n
s
u
s
in
g
m
etr
ics s
u
ch
as
m
ea
n
av
er
ag
e
p
r
ec
is
io
n
(
m
AP)
,
m
u
ltip
le
o
b
ject
tr
ac
k
in
g
ac
cu
r
ac
y
(
MO
T
A)
,
an
d
id
en
tific
a
tio
n
F1
-
s
co
r
e
(
I
DF1
)
.
T
h
e
c
o
m
b
in
atio
n
o
f
th
e
Dee
p
SOR
T
alg
o
r
ith
m
h
elp
s
m
ain
tain
v
eh
icle
r
ec
o
g
n
itio
n
ac
r
o
s
s
co
n
s
ec
u
tiv
e
f
r
am
es,
th
er
eb
y
en
h
an
cin
g
th
e
s
tab
ilit
y
o
f
th
e
m
u
lti
-
o
b
ject
tr
ac
k
in
g
s
y
s
tem
.
A
d
d
itio
n
a
lly
,
th
e
s
licin
g
-
aid
ed
h
y
p
er
in
f
er
e
n
ce
(
SAHI
)
tech
n
iq
u
e
was
in
teg
r
ated
to
im
p
r
o
v
e
t
h
e
d
etec
tio
n
ef
f
icien
c
y
o
f
s
m
all
v
eh
icles
in
r
em
o
te
s
en
s
in
g
im
ag
er
y
an
d
u
r
b
an
s
u
r
v
eillan
ce
v
id
e
o
d
ata
co
llected
in
T
h
ai
Ng
u
y
en
,
Vietn
am
.
T
h
e
r
em
ain
d
e
r
o
f
th
e
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws.
Sectio
n
2
p
r
esen
ts
an
o
v
e
r
v
iew
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
.
Sectio
n
3
d
e
s
cr
ib
es
th
e
ex
p
er
im
en
tal
s
etu
p
an
d
d
etec
tio
n
-
tr
ac
k
i
n
g
m
eth
o
d
s
b
ased
o
n
YOL
Ov
1
1
-
Dee
p
SOR
T
an
d
YOL
Ov
1
1
-
SAHI
.
Sectio
n
4
p
r
esen
ts
ex
p
er
im
en
tal
r
esu
lts
o
n
s
till
im
ag
es
an
d
tr
af
f
ic
s
u
r
v
eillan
ce
v
id
e
o
,
alo
n
g
with
an
aly
s
es
an
d
d
is
cu
s
s
io
n
s
.
Fin
ally
,
s
ec
tio
n
5
p
r
o
v
id
es
co
n
clu
s
io
n
s
an
d
d
ir
ec
tio
n
s
f
o
r
f
u
tu
r
e
r
esear
ch
.
2.
M
E
T
H
O
D
O
VE
RV
I
E
W
2
.
1
.
E
s
t
im
a
t
io
n
t
heo
ry
I
n
(
1
)
an
d
(
2
)
d
escr
ib
e
a
s
et
o
f
s
tates a
n
d
m
ea
s
u
r
em
en
ts
[
2
0
]
.
+
1
=
+
+
(
1
)
+
1
=
+
1
+
+
1
(
2
)
W
h
er
e
r
ep
r
esen
ts
s
y
s
tem
n
o
is
e
an
d
is
m
ea
s
u
r
em
en
t
n
o
is
e.
is
th
e
s
y
s
tem
s
tate
m
atr
i
x
,
is
th
e
in
p
u
t
co
n
tr
o
l
m
atr
ix
,
a
n
d
is
th
e
m
ea
s
u
r
em
en
t
m
atr
ix
.
T
h
e
Kalm
an
f
ilter
is
co
n
s
tr
u
cted
ac
co
r
d
in
g
t
o
th
e
p
r
ed
icto
r
-
c
o
r
r
ec
to
r
m
ec
h
an
is
m
[
2
1
]
.
I
n
th
e
p
r
ed
ictio
n
s
tag
e,
th
e
s
y
s
tem
s
tate
is
esti
m
ated
b
ased
o
n
th
e
p
r
ev
io
u
s
s
tate
v
alu
e
c
o
m
b
in
e
d
with
th
e
s
y
s
tem
'
s
d
y
n
am
ic
m
o
d
el
as
p
r
esen
ted
in
(
3
)
.
T
h
en
,
th
e
s
tate
er
r
o
r
co
v
ar
ian
ce
m
at
r
ix
is
d
eter
m
in
ed
ac
co
r
d
i
n
g
to
(
4
)
.
x
̂
k+
1
|
k
=A
x
̂
k|
k
+
u
k
(
3
)
P
k+
1
|
k
=A
P
k|
k
A
T
+
Q
k
(
4
)
Du
r
in
g
th
e
u
p
d
ate
p
h
ase,
th
e
Kalm
an
g
ain
co
ef
f
icien
t
is
u
s
ed
to
co
r
r
ec
t
th
e
p
r
ed
ictio
n
s
tate
b
ased
o
n
n
ew
m
ea
s
u
r
em
e
n
t
in
f
o
r
m
atio
n
.
T
h
e
esti
m
ated
s
tate
a
n
d
th
e
er
r
o
r
c
o
v
ar
ian
ce
m
atr
ix
ar
e
u
p
d
ated
ac
c
o
r
d
i
n
g
to
(
5
)
an
d
(
6
)
,
r
esp
ec
tiv
ely
.
̂
+
1
|
+
1
=
̂
+
1
|
+
+
1
(
+
1
−
+
1
̂
+
1
|
)
(
5
)
+
1
|
+
1
=
(
−
+
1
+
1
)
+
1
|
(
−
+
1
+
1
)
+
+
1
+
1
|
+
1
(
6
)
W
h
er
e
is
an
id
en
tity
m
atr
ix
o
f
s
ize
×
with
b
ein
g
th
e
n
u
m
b
er
o
f
s
tates
o
f
th
e
s
y
s
tem
;
r
ep
r
e
s
en
ts
th
e
tim
e
s
tep
;
|
co
r
r
esp
o
n
d
s
to
th
e
v
alu
e
u
p
d
ated
at
th
e
p
r
ev
i
o
u
s
iter
atio
n
,
+
1
|
r
ep
r
esen
ts
th
e
p
r
ed
icted
v
alu
e
at
tim
e
+
1
.
2
.
2
.
O
v
er
v
iew
o
f
Dee
pS
O
RT
a
lg
o
rit
hm
T
h
e
Dee
p
SOR
T
tr
ac
k
in
g
p
r
o
c
ess
i
s
illu
s
tr
ated
in
Fig
u
r
e
1
[
2
2
]
.
A
s
eq
u
en
ce
o
f
v
id
eo
f
r
am
e
s
is
in
p
u
t
,
an
d
th
e
e
n
d
p
o
in
t
is
ch
ec
k
ed
.
T
h
e
d
etec
ted
f
r
am
es
ar
e
c
o
m
p
ar
ed
with
t
h
e
p
r
e
d
icted
f
r
am
es
b
ased
o
n
th
e
in
ter
s
ec
tio
n
o
v
e
r
u
n
io
n
(
I
o
U
)
in
d
e
x
,
t
h
er
eb
y
cr
ea
tin
g
a
co
s
t
m
atr
ix
.
T
h
is
m
atr
ix
is
p
r
o
ce
s
s
ed
b
y
th
e
Hu
n
g
ar
ian
alg
o
r
ith
m
to
f
in
d
th
e
o
p
tim
al
m
atc
h
in
g
p
air
s
f
o
r
th
r
ee
ca
s
es:
tr
ajec
to
r
y
m
is
m
atch
,
d
etec
tio
n
m
is
m
atch
,
an
d
tr
ajec
to
r
y
m
at
ch
.
W
h
en
th
er
e
is
a
m
atc
h
,
th
e
Kalm
an
f
ilter
is
u
s
ed
to
u
p
d
ate
an
d
p
r
ed
ict
th
e
s
tate
o
f
th
e
o
b
jects.
T
h
is
m
atch
in
g
an
d
u
p
d
atin
g
p
r
o
ce
s
s
is
r
ep
ea
ted
co
n
tin
u
o
u
s
ly
u
n
til
th
e
tr
ajec
to
r
y
o
f
th
e
o
b
ject
is
co
n
f
ir
m
e
d
o
r
th
e
v
id
e
o
en
d
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
5
,
Au
g
u
s
t 2
0
2
6
:
3
5
1
8
-
3
5
2
7
3520
Fig
u
r
e
1
.
Veh
icle
tr
ac
k
in
g
is
b
ased
o
n
Dee
p
SOR
T
alg
o
r
ith
m
3.
E
XP
E
R
I
M
E
N
T
A
L
SE
T
UP
AND
E
VA
L
UA
T
I
O
N
3
.
1
.
E
x
perim
ent
a
l
d
a
t
a
s
et
All
im
ag
es
wer
e
co
llected
f
r
o
m
r
ea
l
-
wo
r
ld
u
r
b
an
tr
a
f
f
ic
in
T
h
ai
Ng
u
y
en
,
Vietn
am
.
Data
,
in
clu
d
in
g
b
o
th
im
ag
es
an
d
v
id
eo
s
,
wer
e
co
llected
u
s
in
g
u
r
b
a
n
tr
af
f
ic
s
u
r
v
eillan
ce
ca
m
er
as
an
d
ca
m
er
as
in
teg
r
ated
in
to
u
n
m
an
n
ed
ae
r
ial
v
e
h
icles
(
U
AVs)
.
I
m
ag
e
co
llectio
n
was
co
n
d
u
cte
d
at
v
ar
io
u
s
tim
es
o
f
th
e
d
ay
an
d
u
n
d
er
d
if
f
er
en
t
tr
a
f
f
ic
co
n
d
itio
n
s
(
s
u
ch
as
v
eh
icle
d
e
n
s
ity
,
wea
th
er
co
n
d
itio
n
s
,
a
n
d
p
ar
tial
o
cc
lu
s
io
n
)
.
T
h
e
co
llected
d
ata
was
u
s
ed
to
ev
alu
ate
v
eh
icle
d
etec
tio
n
an
d
m
u
lti
-
o
b
ject
tr
ac
k
in
g
p
er
f
o
r
m
a
n
ce
in
p
r
ac
tical
I
T
S
ap
p
licatio
n
s
s
u
ch
as
tr
af
f
ic
m
o
n
ito
r
in
g
,
v
e
h
icle
co
u
n
tin
g
,
a
n
d
f
lo
w
an
aly
s
is
.
A
p
o
r
tio
n
o
f
th
e
co
llected
d
ata
was
m
an
u
ally
an
n
o
tated
to
q
u
an
titativ
ely
ass
ess
th
e
d
etec
tio
n
an
d
tr
ac
k
in
g
p
er
f
o
r
m
a
n
ce
o
f
th
e
test
s
ce
n
ar
io
s
.
T
h
e
r
em
ai
n
in
g
d
ata
was
u
s
ed
f
o
r
e
x
p
er
im
e
n
tal
an
al
y
s
is
an
d
e
v
alu
atio
n
o
f
th
e
p
r
o
p
o
s
e
d
s
y
s
tem
.
T
h
e
test
s
ce
n
ar
io
s
wer
e
im
p
lem
en
ted
b
ased
o
n
a
cu
s
to
m
ize
d
YOL
O
m
o
d
el
(
1
0
0
e
p
o
ch
s
,
1
6
b
atc
h
e
s
)
.
3
.
2
.
YO
L
O
v
1
1
-
ba
s
ed
o
bje
ct
det
ec
t
io
n a
nd
m
ulti
-
o
bje
ct
t
ra
ck
ing
wit
h De
epSO
RT
Fig
u
r
e
2
illu
s
tr
ates
th
e
ar
ch
it
ec
tu
r
e
o
f
th
e
YOL
Ov
1
1
m
o
d
el
an
d
th
e
in
teg
r
atio
n
p
r
o
ce
s
s
with
th
e
Dee
p
SOR
T
alg
o
r
ith
m
.
As
s
h
o
wn
in
Fig
u
r
e
2
(
a)
,
YOL
Ov
1
1
is
b
u
ilt
with
s
p
atial
p
y
r
am
id
p
o
o
lin
g
–
f
ast
(
SPPF
)
,
C
2
with
p
ar
tial
s
p
atial
atten
tio
n
(
C
2
PS
A
),
an
d
C
3
with
k
er
n
el
s
ize
2
(
C
3
K
2
)
m
o
d
u
les
[
2
3
]
to
en
h
an
ce
f
ea
tu
r
e
ex
tr
ac
tio
n
ca
p
ab
ilit
ies
an
d
o
b
ject
d
etec
tio
n
ef
f
icien
cy
in
co
m
p
lex
tr
af
f
ic
s
ce
n
ar
io
s
.
T
o
im
p
r
o
v
e
m
u
lti
-
o
b
ject
tr
ac
k
in
g
e
f
f
icien
cy
,
th
e
Dee
p
SOR
T
alg
o
r
ith
m
i
n
teg
r
atio
n
p
r
o
ce
s
s
is
illu
s
tr
ated
in
Fig
u
r
e
2
(
b
)
.
I
n
th
is
p
r
o
ce
s
s
,
YOL
Ov
1
1
p
er
f
o
r
m
s
v
eh
icle
d
etec
tio
n
o
n
ea
ch
f
r
a
m
e
in
d
ep
en
d
e
n
tly
,
w
h
ile
Dee
p
SOR
T
h
an
d
les lin
k
in
g
an
d
m
ain
tain
in
g
o
b
ject
id
e
n
tific
atio
n
b
etwe
en
co
n
s
ec
u
tiv
e
f
r
am
es.
(
a)
(
b
)
Fig
u
r
e
2
.
YOL
Ov
1
1
m
o
d
el
ar
ch
itectu
r
e
an
d
i
n
teg
r
atio
n
m
ec
h
an
is
m
with
th
e
Dee
p
SOR
T
alg
o
r
ith
m
:
(
a)
YOL
Ov
1
1
m
o
d
el
ar
c
h
itectu
r
e
an
d
(
b
)
YOL
Ov
1
1
c
o
m
b
in
ed
with
th
e
Dee
p
SOR
T
alg
o
r
it
h
m
3
.
3
.
S
m
a
ll
o
bje
ct
det
ec
t
io
n ba
s
ed
o
n YO
L
O
v
1
1
a
nd
SAH
I
t
ec
hn
iq
ue
Fig
u
r
e
3
s
h
o
ws
an
o
v
er
v
iew
o
f
th
e
im
a
g
e
s
licin
g
-
b
ased
r
ef
in
em
en
t
a
n
d
in
f
e
r
en
ce
p
ip
elin
e.
As
illu
s
tr
ated
in
Fig
u
r
e
3
(
a)
,
d
u
r
i
n
g
th
e
tr
ain
in
g
p
h
ase,
th
e
in
p
u
t
im
ag
e
is
d
iv
id
ed
in
t
o
o
v
e
r
lap
p
in
g
s
lices,
th
u
s
in
cr
ea
s
in
g
th
e
r
ep
r
esen
tatio
n
r
ate
o
f
s
m
all
o
b
jects
in
ea
ch
i
m
ag
e
r
eg
io
n
.
I
n
th
e
in
f
er
e
n
ce
p
h
ase,
d
escr
ib
ed
in
Fig
u
r
e
3
(
b
)
,
th
e
in
p
u
t
im
ag
e
i
s
f
u
r
th
er
d
i
v
id
ed
in
to
s
m
aller
s
lices
b
ef
o
r
e
b
ein
g
f
ed
in
to
th
e
YOL
Ov
1
1
m
o
d
el
f
o
r
o
b
ject
d
etec
tio
n
i
n
ea
c
h
in
d
ep
en
d
e
n
t
im
ag
e
r
e
g
io
n
.
Su
b
s
eq
u
en
tly
,
th
e
d
etec
tio
n
r
esu
lts
f
r
o
m
all
s
lices
ar
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
P
erfo
r
ma
n
ce
ev
a
lu
a
tio
n
o
f YOLOv1
1
-
b
a
s
ed
ve
h
icle
d
etec
tio
n
a
n
d
tr
a
ck
in
g
fo
r
u
r
b
a
n
…
(
T
h
a
n
g
C
.
V
u
)
3521
co
m
b
in
ed
a
n
d
p
r
o
ce
s
s
ed
u
s
in
g
th
e
n
o
n
-
m
ax
im
u
m
s
u
p
p
r
ess
io
n
(
NM
S)
alg
o
r
ith
m
to
eli
m
in
ate
o
v
er
lap
p
in
g
p
r
ed
ictio
n
s
an
d
p
r
o
d
u
ce
a
f
in
a
l d
etec
tio
n
r
esu
lt with
h
ig
h
er
a
cc
u
r
ac
y
.
(
a)
(
b
)
Fig
u
r
e
3
.
YOL
Ov
1
1
an
d
SAHI
-
b
ased
s
m
all
o
b
ject
d
etec
tio
n
:
(
a)
im
ag
e
s
licin
g
-
b
ased
r
ef
in
e
m
en
t stag
e
an
d
(
b
)
im
ag
e
s
licin
g
-
b
ased
in
f
e
r
e
n
ce
s
tag
e
Per
i
m
ag
e
1
,
2
,
…
,
cu
t
in
to
o
v
er
lap
p
i
n
g
p
atch
es
1
,
2
,
…
with
f
ix
ed
s
izes
an
d
.
Du
r
in
g
th
e
f
in
e
-
tu
n
in
g
p
h
ase,
th
ese
s
lices
ar
e
r
esized
wh
ile
m
ain
tain
in
g
th
e
o
r
ig
in
al
asp
e
ct
r
atio
to
en
s
u
r
e
co
n
s
is
ten
cy
in
s
p
atial
f
ea
tu
r
es.
T
h
en
,
th
e
en
h
a
n
ce
d
im
ag
es
a
lo
n
g
with
th
e
o
r
ig
in
al
im
ag
e
a
r
e
u
s
ed
to
tr
ain
th
e
m
o
d
el
[
2
4
]
,
[
2
5
]
.
T
h
is
ap
p
r
o
a
ch
im
p
r
o
v
es
f
ea
tu
r
e
lear
n
in
g
ca
p
ab
ilit
ies
f
o
r
s
m
all
o
b
jects
ap
p
ea
r
in
g
in
tr
af
f
ic
m
o
n
ito
r
in
g
d
ata.
Sli
cin
g
tech
n
iq
u
es
ar
e
also
a
p
p
lied
in
th
e
in
f
er
en
ce
p
h
ase.
Sp
ec
if
ically
,
th
e
in
p
u
t
q
u
er
y
i
m
ag
e
I
is
s
p
lit
in
to
m
u
ltip
le
o
v
er
lap
p
in
g
s
lices
1
,
2
,
…
with
d
im
en
s
io
n
s
×
.
E
a
ch
s
lice
is
r
esized
an
d
p
ass
ed
th
r
o
u
g
h
th
e
YOL
Ov
1
1
m
o
d
e
l
f
o
r
in
d
ep
en
d
en
t
o
b
ject
d
et
ec
tio
n
.
Ad
d
itio
n
ally
,
f
u
ll
in
f
er
en
ce
(
FI)
ca
n
b
e
p
er
f
o
r
m
ed
o
n
th
e
o
r
ig
in
al
im
ag
e
to
aid
in
th
e
d
etec
tio
n
o
f
lar
g
er
o
b
jects.
Fin
ally
,
th
e
d
etec
tio
n
r
esu
lts
f
r
o
m
th
e
s
lices a
n
d
th
e
o
r
ig
in
al
i
m
a
g
e
ar
e
m
ap
p
ed
b
ac
k
to
t
h
e
o
r
i
g
in
al
im
ag
e
s
ize
an
d
m
er
g
e
d
u
s
in
g
NM
S.
4.
SI
M
UL
A
T
I
O
N
R
E
S
UL
T
S
4
.
1
.
St
a
t
ic
ima
g
e
-
ba
s
ed
o
bje
ct
det
ec
t
io
n
Fig
u
r
e
4
illu
s
tr
ates th
e
ex
p
er
i
m
en
tal
r
esu
lts
o
f
th
e
p
r
o
p
o
s
ed
s
y
s
tem
f
r
am
ewo
r
k
.
T
h
e
d
etec
tio
n
r
esu
lts
o
b
tain
ed
u
s
in
g
th
e
YOL
Ov
5
m
o
d
el
ar
e
s
h
o
wn
in
Fig
u
r
e
4
(
a)
,
wh
er
e
s
o
m
e
s
m
all
o
r
p
ar
tially
o
b
s
cu
r
ed
v
eh
icles
wer
e
m
is
s
ed
d
esp
ite
th
e
f
aster
in
f
er
en
ce
s
p
ee
d
o
f
3
1
.
5
m
s
.
I
n
co
n
tr
ast,
Fig
u
r
e
4
(
b
)
s
h
o
ws
th
at
th
e
YOL
Ov
1
1
m
o
d
el
ac
h
iev
ed
m
o
r
e
r
eliab
le
d
etec
tio
n
with
a
h
ig
h
er
co
n
f
id
e
n
ce
s
co
r
e,
al
th
o
u
g
h
it
r
eq
u
ir
e
d
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
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tell
,
Vo
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14
,
No
.
5
,
Au
g
u
s
t 2
0
2
6
:
3
5
1
8
-
3
5
2
7
3522
lo
n
g
er
in
f
er
e
n
ce
tim
e
o
f
8
3
.
3
m
s
.
Fu
r
th
er
m
o
r
e,
u
n
o
b
s
tr
u
cte
d
o
b
jects
ac
h
iev
ed
co
n
f
id
e
n
ce
s
co
r
es
u
p
t
o
0
.
9
2
,
wh
ile
s
m
aller
o
r
o
b
s
cu
r
e
d
v
e
h
icles
m
ain
tain
ed
co
n
f
id
en
ce
v
alu
es
r
an
g
in
g
f
r
o
m
0
.
4
7
t
o
0
.
7
6
.
Ho
wev
er
,
t
h
e
co
s
t o
f
th
e
YOL
Ov
1
1
m
o
d
el
is
th
e
in
cr
ea
s
ed
co
m
p
u
tatio
n
al
co
m
p
lex
ity
a
n
d
h
i
g
h
er
r
eso
u
r
c
e
r
eq
u
ir
em
e
n
ts
.
(
a)
(
b
)
Fig
u
r
e
4
.
Ob
ject
d
etec
tio
n
r
es
u
lts
f
r
o
m
a
d
i
g
ital im
ag
e
(
p
h
o
t
o
tak
en
in
ce
n
tr
al
T
h
ai
N
g
u
y
e
n
,
Vietn
am
)
:
(
a)
YOL
Ov
5
m
o
d
el
an
d
(
b
)
Y
OL
Ov
1
1
m
o
d
el
4
.
2
.
T
ra
c
k
v
ehicles in t
ra
f
f
ic
m
o
nito
ring
v
ideo
ba
s
ed
o
n
YO
L
O
v
1
1
-
Dee
pS
O
RT
T
h
is
s
ec
tio
n
p
r
esen
ts
s
o
m
e
ex
p
er
im
en
tal
r
esu
lts
o
f
th
e
p
r
o
p
o
s
ed
s
y
s
tem
f
r
am
ewo
r
k
f
o
r
th
e
p
r
o
b
lem
o
f
d
etec
tin
g
a
n
d
tr
ac
k
in
g
v
e
h
icles
f
r
o
m
s
u
r
v
eillan
ce
ca
m
er
as.
T
h
e
ex
p
e
r
im
en
tal
r
esu
lts
o
n
r
o
ad
s
in
T
h
ai
Ng
u
y
en
,
Vietn
am
ar
e
illu
s
tr
ated
in
Fig
u
r
e
5
.
Af
ter
a
v
eh
icle
is
d
etec
ted
b
y
YOL
Ov
1
1
,
th
e
s
y
s
tem
ass
ig
n
s
a
u
n
iq
u
e
id
en
tifie
r
(
I
D)
to
ea
ch
o
b
ject
to
s
u
p
p
o
r
t
tr
ac
k
in
g
m
u
ltip
le
o
b
jects
in
co
n
s
ec
u
tiv
e
f
r
am
es
.
As
s
h
o
wn
in
Fig
u
r
e
5
(
a
)
,
th
e
s
y
s
tem
co
n
tin
u
o
u
s
ly
tr
ac
k
s
ea
ch
I
D
in
s
eq
u
e
n
tial
f
r
am
e
s
.
Fu
r
th
er
m
o
r
e,
as
d
escr
ib
ed
in
Fig
u
r
e
5
(
b
)
,
th
e
s
y
s
tem
en
ab
les
th
e
ef
f
ec
tiv
e
d
etec
tio
n
an
d
tr
ac
k
in
g
o
f
p
ar
t
ially
o
r
co
m
p
letely
o
b
s
cu
r
ed
v
eh
icles
b
y
ap
p
ly
in
g
a
Kalm
an
f
ilter
.
I
n
ad
d
itio
n
,
YOL
Ov
1
1
s
h
o
ws
ef
f
ec
tiv
e
o
b
ject
d
etec
tio
n
,
esp
ec
ially
with
o
b
jects
th
at
a
r
e
q
u
ite
s
m
all
in
s
ize
o
r
o
n
ly
p
ar
tially
v
is
ib
le
in
th
e
f
r
am
e.
Af
ter
th
e
o
b
ject
d
etec
tio
n
p
r
o
ce
s
s
o
f
th
e
g
iv
e
n
m
o
d
el
is
co
m
p
leted
,
th
e
v
id
eo
o
u
tp
u
t
with
t
h
e
d
etec
ted
o
b
jects
is
id
en
tifie
d
,
an
d
th
e
p
ar
am
eter
s
o
f
th
e
n
u
m
b
er
o
f
v
eh
icles a
r
e
d
is
p
lay
e
d
.
(
a)
(
b
)
Fig
u
r
e
5
.
Veh
icle
tr
ac
k
in
g
r
esu
lts
u
s
in
g
th
e
YOL
Ov
1
1
-
Dee
p
SOR
T
f
r
am
ewo
r
k
o
n
tr
af
f
ic
s
u
r
v
eillan
ce
v
id
e
o
s
:
(
a)
co
n
s
is
ten
t v
eh
icle
I
D
ass
ig
n
m
en
t a
cr
o
s
s
co
n
s
ec
u
tiv
e
f
r
a
m
es
an
d
(
b
)
r
o
b
u
s
t tr
ac
k
in
g
p
e
r
f
o
r
m
a
n
ce
u
n
d
er
p
ar
tial o
cc
lu
s
io
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
P
erfo
r
ma
n
ce
ev
a
lu
a
tio
n
o
f YOLOv1
1
-
b
a
s
ed
ve
h
icle
d
etec
tio
n
a
n
d
tr
a
ck
in
g
fo
r
u
r
b
a
n
…
(
T
h
a
n
g
C
.
V
u
)
3523
T
h
e
s
y
s
tem
'
s
v
id
eo
p
r
o
ce
s
s
in
g
p
er
f
o
r
m
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n
ce
was
ev
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ate
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u
s
in
g
f
r
am
es
p
e
r
s
ec
o
n
d
(
FP
S).
T
h
e
ex
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er
im
en
tal
h
ar
d
war
e
s
y
s
tem
was
b
ased
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n
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I
n
tel
C
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r
e
i5
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r
o
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d
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5
6
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B
o
f
s
to
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ag
e.
T
h
e
r
esu
lts
in
T
ab
le
1
s
h
o
w
an
im
p
r
o
v
em
e
n
t
in
th
e
d
etec
tio
n
ac
cu
r
ac
y
o
f
YOL
Ov
1
1
co
m
p
a
r
ed
to
s
o
m
e
p
r
ev
io
u
s
g
en
er
ati
o
n
m
o
d
els.
Simu
ltan
eo
u
s
ly
,
th
e
in
teg
r
atio
n
o
f
th
e
Dee
p
SOR
T
alg
o
r
ith
m
s
ig
n
if
ic
an
tly
en
h
a
n
ce
d
m
u
lti
-
o
b
ject
tr
ac
k
in
g
e
f
f
icien
cy
,
r
ef
lecte
d
in
h
ig
h
er
MO
T
A
an
d
I
DF1
.
I
n
p
ar
ticu
lar
,
th
e
YOL
Ov
1
1
-
Dee
p
SOR
T
co
m
b
i
n
ed
m
o
d
el
d
e
m
o
n
s
tr
ated
an
e
f
f
ec
t
iv
e
b
alan
ce
b
etwe
en
d
etec
tio
n
ac
cu
r
ac
y
,
r
ec
o
g
n
itio
n
co
n
s
is
ten
cy
,
a
n
d
tr
ac
k
in
g
s
tab
ilit
y
in
a
c
o
m
p
lex
u
r
b
a
n
tr
af
f
ic
en
v
ir
o
n
m
en
t.
I
n
ter
m
s
o
f
co
m
p
u
tin
g
p
e
r
f
o
r
m
an
ce
,
th
e
s
y
s
tem
ac
h
iev
ed
a
p
r
o
ce
s
s
in
g
s
p
ee
d
o
f
ap
p
r
o
x
i
m
ately
1
0
.
1
3
FP
S.
YOL
Ov
1
1
h
as
a
h
ig
h
er
m
o
d
el
co
m
p
le
x
ity
,
r
esu
ltin
g
in
l
o
wer
p
r
o
ce
s
s
in
g
s
p
ee
d
s
co
m
p
a
r
ed
to
li
g
h
ter
v
er
s
io
n
s
o
f
YOL
O
.
T
ab
le
1
.
So
m
e
r
esu
lts
co
m
p
ar
i
n
g
th
e
c
o
m
b
in
atio
n
o
f
YOL
O
m
o
d
els with
th
e
Dee
p
SOR
T
alg
o
r
ith
m
M
e
t
h
o
d
mA
P
@
0
.
5
mA
P
@
0
.
5
:
0
.
9
5
M
O
TA
I
D
F
1
FPS
Y
O
LO
v
5
+
D
e
e
p
S
O
R
T
0
.
5
8
0
.
3
6
0
.
6
2
0
.
6
6
1
9
.
2
Y
O
LO
v
9
+
D
e
e
p
S
O
R
T
0
.
6
3
0
.
4
2
0
.
6
8
0
.
7
0
1
3
.
4
Y
O
LO
v
1
1
+
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e
e
p
S
O
R
T
0
.
6
6
0
.
4
5
0
.
7
0
0
.
7
3
1
0
.
1
T
o
ev
al
u
ate
th
e
s
y
s
tem
'
s
ac
cu
r
ac
y
,
a
m
a
n
u
al
v
alid
atio
n
p
r
o
ce
s
s
was
p
er
f
o
r
m
e
d
o
n
a
s
ele
cted
s
u
b
s
et
o
f
th
e
test
v
id
eo
d
ataset.
Sp
e
cif
ically
,
v
id
eo
s
eg
m
en
ts
with
a
to
tal
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u
r
atio
n
o
f
ap
p
r
o
x
i
m
ately
o
n
e
m
in
u
te
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ap
p
r
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x
im
ately
4
,
5
0
0
f
r
am
es
i
n
to
tal)
wer
e
s
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ted
f
o
r
e
x
p
er
im
en
tatio
n
in
th
is
s
ce
n
ar
io
.
I
n
th
is
p
r
o
ce
s
s
,
th
e
n
u
m
b
er
o
f
v
eh
icles
was
m
an
u
ally
co
u
n
te
d
as
v
eh
icles
p
a
s
s
ed
th
r
o
u
g
h
estab
lis
h
ed
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r
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al
zo
n
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an
d
th
e
r
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lts
wer
e
co
m
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ar
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with
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h
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ex
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im
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tal
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lts
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th
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y
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tem
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x
p
e
r
im
e
n
tal
r
esu
lts
s
h
o
wed
th
at
th
e
s
y
s
tem
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s
o
u
tp
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t
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h
iev
ed
an
av
er
a
g
e
er
r
o
r
r
ate
o
f
less
th
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5
%
(
T
ab
le
2
)
.
Ho
wev
er
,
in
s
o
m
e
ex
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er
im
en
tal
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itu
atio
n
s
,
th
e
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lts
wer
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f
ec
ted
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y
s
h
o
r
t
-
ter
m
o
cc
lu
s
io
n
o
r
th
e
p
a
r
tial
ap
p
ea
r
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ce
o
f
v
eh
icles a
t th
e
ed
g
e
o
f
th
e
f
r
a
m
e.
T
ab
le
2
.
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h
e
r
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lts
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u
n
t th
e
n
u
m
b
er
o
f
v
eh
icles u
s
in
g
th
e
i
n
teg
r
ated
YOL
Ov
1
1
-
Dee
p
SO
R
T
f
r
am
ewo
r
k
V
i
d
e
o
c
o
d
e
M
a
n
u
a
l
c
o
u
n
t
S
y
st
e
m c
o
u
n
t
A
b
so
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t
e
e
r
r
o
r
Er
r
o
r
r
a
t
e
(
%)
V
i
d
e
o
1
70
67
3
4
.
2
9
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i
d
e
o
2
74
72
2
2
.
7
0
V
i
d
e
o
3
61
58
3
4
.
9
2
4
.
3
.
O
bje
c
t
det
ec
t
io
n ba
s
ed
o
n sta
t
ic
re
m
o
t
e
s
ens
ing
im
a
g
es
T
h
e
s
im
u
latio
n
o
f
t
h
e
YOL
Ov
1
1
h
y
b
r
i
d
p
r
o
g
r
a
m
with
a
n
a
d
v
an
ce
d
i
n
f
er
en
c
e
alg
o
r
ith
m
u
s
in
g
im
ag
e
s
licin
g
in
o
b
ject
d
etec
tio
n
f
r
o
m
r
em
o
te
s
en
s
in
g
im
ag
es
was
p
er
f
o
r
m
ed
b
y
th
e
au
th
o
r
s
o
n
th
e
Go
o
g
le
C
o
lab
p
latf
o
r
m
(
allo
win
g
ex
ec
u
tio
n
o
f
Py
th
o
n
co
m
m
a
n
d
s
o
n
th
e
c
lo
u
d
p
latf
o
r
m
)
.
T
h
e
GPU
p
ar
a
m
eter
s
s
elec
ted
ar
e
as
f
o
llo
ws:
GPU
(
T
4
)
,
R
AM
(
1
5
GB
)
.
T
h
e
s
et
o
f
s
till
im
ag
es
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s
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in
th
e
s
tu
d
y
is
in
J
PEG
f
o
r
m
at
(
.
p
n
g
/.j
p
g
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eg
)
,
with
a
u
n
if
o
r
m
s
ize
o
f
1
2
7
6
×
7
8
8
p
i
x
els.
T
h
is
is
an
in
itial
test
d
at
aset
co
llected
in
a
co
n
tr
o
lled
m
a
n
n
er
,
s
u
itab
le
f
o
r
v
er
if
y
i
n
g
th
e
a
p
p
licatio
n
p
o
t
en
tial
o
f
th
e
s
y
s
tem
.
T
h
e
im
a
g
es
ar
e
s
elec
ted
to
s
im
u
late
u
r
b
an
tr
a
f
f
ic
s
itu
atio
n
s
with
lo
w
to
m
ed
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m
o
b
jec
t
d
en
s
ity
,
r
ef
lectin
g
r
ea
l
u
r
b
a
n
co
n
d
itio
n
s
,
wh
ile
en
s
u
r
in
g
th
e
p
r
esen
ce
o
f
m
an
y
s
m
all
o
b
jects.
I
n
Fig
u
r
e
6
,
o
b
ject
d
etec
tio
n
ex
p
er
im
e
n
ts
wer
e
co
n
d
u
cted
u
s
in
g
th
e
YOL
Ov
1
1
m
o
d
el
o
n
ae
r
ial
p
h
o
to
g
r
ap
h
s
tak
en
in
T
h
ai
Ng
u
y
en
,
Vietn
am
.
Fig
u
r
e
6
(
a)
p
r
esen
ts
th
e
v
eh
icle
d
etec
tio
n
r
esu
lts
b
ased
o
n
th
e
YOL
Ov
1
1
m
o
d
el
alo
n
e.
I
n
th
is
s
ce
n
ar
io
,
s
o
m
e
o
b
jects
wer
e
m
is
s
ed
d
u
e
to
th
eir
s
m
all
s
ize
o
r
s
tr
o
n
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o
cc
lu
s
io
n
.
C
o
n
v
e
r
s
ely
,
Fig
u
r
e
6
(
b
)
s
h
o
ws
th
at
co
m
b
in
i
n
g
th
e
YOL
Ov
1
1
m
o
d
el
with
t
h
e
SAHI
tech
n
iq
u
e
s
ig
n
if
ican
tly
in
cr
ea
s
ed
t
h
e
n
u
m
b
er
o
f
d
etec
ted
o
b
jects,
in
clu
d
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g
s
m
all,
d
is
tan
t,
an
d
p
ar
tially
o
cc
lu
d
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d
v
eh
icles.
I
n
th
e
test
s
ce
n
ar
io
,
th
e
co
n
f
id
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ce
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g
ed
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r
o
m
0
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5
7
to
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8
9
,
in
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icatin
g
th
at
th
e
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y
s
tem
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s
u
f
f
icien
tly
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en
s
itiv
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an
d
m
ain
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ed
its
f
ilter
in
g
ca
p
ab
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.
T
ab
le
3
co
m
p
ar
es
th
e
d
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o
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er
f
o
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o
f
YOL
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1
1
with
an
d
with
o
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t
th
e
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tr
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o
n
ae
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ial
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ag
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T
h
e
r
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d
icate
th
at
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o
r
p
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atin
g
SAHI
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ig
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if
ican
tly
im
p
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d
et
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tio
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ac
cu
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f
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m
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icles.
T
h
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im
p
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v
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en
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d
em
o
n
s
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ates
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f
ec
tiv
en
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r
s
m
all
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d
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.
4
.
4
.
Aer
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l
v
ideo
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ba
s
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o
bje
ct
det
ec
t
io
n
T
h
e
ex
p
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im
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was
co
n
d
u
cte
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o
n
a
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ataset
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tr
ac
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r
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m
UAV
v
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r
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o
r
d
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in
t
h
e
i
n
n
er
city
o
f
T
h
ai
Ng
u
y
en
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Vietn
am
.
T
h
e
e
x
tr
ac
ted
v
id
eo
s
ar
e
in
MP4
f
o
r
m
at
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ec
o
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h
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e
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v
id
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in
to
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s
ize
with
en
o
u
g
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o
v
er
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to
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o
t
m
is
s
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o
d
el
f
o
r
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d
e
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en
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t
o
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.
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h
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r
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e
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en
m
e
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g
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n
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ig
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d
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o
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h
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o
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o
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e
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.
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x
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tal
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o
w
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at
th
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y
s
tem
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ec
tiv
ely
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m
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s
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u
ch
as
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ca
r
s
,
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d
m
o
to
r
cy
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ac
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o
s
s
th
e
en
tire
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ep
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e
im
ag
e.
T
h
e
d
etec
tio
n
b
o
u
n
d
ar
ies
ar
e
d
is
p
lay
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alo
n
g
with
th
eir
c
o
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r
esp
o
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d
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g
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lar
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er
o
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jects
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e
n
er
ally
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v
in
g
h
ig
h
e
r
r
eliab
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lev
els,
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an
g
in
g
f
r
o
m
0
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8
3
–
0
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9
2
.
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h
e
m
in
im
u
m
d
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tio
n
th
r
esh
o
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et
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at
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to
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p
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in
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af
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o
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illu
s
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ated
in
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u
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jects
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e
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ig
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e
n
er
ally
y
ield
lo
wer
r
eliab
ilit
y
r
esu
lts
co
m
p
ar
ed
to
lar
g
er
v
eh
icles
(
0
.
3
-
0
.
5
)
.
Fu
r
th
er
m
o
r
e
,
wh
en
u
s
in
g
o
n
ly
th
e
YOL
Ov
1
1
m
o
d
el,
s
o
m
e
s
m
all
o
r
h
id
d
en
o
b
jects m
ay
n
o
t b
e
f
u
lly
d
etec
ted
.
(
a)
(
b
)
Fig
u
r
e
6
.
Sm
all
o
b
ject
d
etec
tio
n
r
esu
lts
in
r
em
o
te
s
en
s
in
g
i
m
ag
es in
T
h
ai
Ng
u
y
en
,
Vietn
am
: (
a)
d
etec
tio
n
r
esu
lts
b
ased
o
n
th
e
YOL
Ov
1
1
m
o
d
el
a
n
d
(
b
)
d
etec
tio
n
r
esu
lts
u
s
in
g
YOL
Ov
1
1
co
m
b
in
ed
with
th
e
SAHI
tech
n
iq
u
e
u
s
in
g
th
e
im
ag
e
s
licin
g
m
eth
o
d
T
ab
le
3
.
C
o
m
p
a
r
ativ
e
r
esu
lts
o
f
YOL
Ov
1
1
a
n
d
YOL
Ov
1
1
c
o
m
b
in
ed
with
SAHI
f
o
r
s
m
all
o
b
ject
d
etec
tio
n
C
o
n
f
i
g
u
r
a
t
i
o
n
mA
P
@
0
.
5
mA
P
@
0
.
5
:
0
.
9
5
A
P
_
S
Y
O
LO
v
1
1
0
.
6
6
0
.
4
5
0
.
2
9
Y
O
LO
v
1
1
+
S
A
H
I
0
.
7
0
0
.
4
8
0
.
4
0
Fig
u
r
e
7
.
Sam
p
le
o
b
ject
d
etec
t
io
n
r
esu
lts
o
f
YOL
Ov
1
1
with
SAHI
o
n
UAV
tr
af
f
ic
d
ata
in
T
h
ai
Ng
u
y
e
n
,
Vietn
am
4
.
5
.
Dis
cus
s
io
n
E
x
p
er
im
en
tal
r
esu
lts
s
h
o
w
th
at
th
e
YOL
Ov
1
1
m
o
d
el,
co
m
b
in
ed
with
th
e
Dee
p
SOR
T
alg
o
r
ith
m
,
r
eliab
ly
d
etec
ts
an
d
tr
ac
k
s
v
eh
icles
in
u
r
b
an
tr
af
f
ic
v
id
e
o
.
T
h
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
f
r
am
ewo
r
k
m
ain
tain
s
o
b
ject
r
ec
o
g
n
itio
n
ac
r
o
s
s
co
n
s
ec
u
ti
v
e
f
r
a
m
es.
T
h
e
u
s
e
o
f
Kalm
an
f
ilter
co
n
t
r
ib
u
tes
to
m
ain
tain
in
g
tr
ajec
to
r
y
co
n
tin
u
ity
wh
en
v
eh
icles
ar
e
p
ar
tially
o
b
s
cu
r
ed
.
Ho
wev
er
,
ex
p
er
im
en
tal
r
esu
lts
s
h
o
w
th
at
v
id
eo
q
u
ality
an
d
f
r
am
e
r
eso
lu
tio
n
s
ig
n
if
ican
tly
im
p
ac
t sy
s
tem
p
er
f
o
r
m
an
ce
.
I
n
test
s
ce
n
ar
io
s
u
s
in
g
th
e
YOL
Ov
1
1
m
o
d
el
with
SAHI
tech
n
o
lo
g
y
,
th
e
r
eliab
ilit
y
s
co
r
e
o
f
d
etec
tio
n
r
esu
lts
was
r
elativ
ely
h
ig
h
f
o
r
v
eh
icles
clea
r
ly
v
is
ib
le
in
s
till
im
ag
es.
Ho
wev
er
,
r
eliab
ilit
y
r
esu
lts
wer
e
lo
wer
,
esp
ec
ially
f
o
r
s
m
aller
o
r
d
is
tan
t
v
eh
icles
in
test
s
ce
n
ar
io
s
with
co
llected
v
id
e
o
s
eq
u
en
ce
s
.
T
h
e
m
ain
f
ac
to
r
s
af
f
ec
tin
g
t
h
e
ex
p
er
im
en
tal
r
esu
lts
ca
n
b
e
m
en
tio
n
ed
as:
s
lig
h
t
f
lu
ctu
atio
n
s
o
f
th
e
UAV
ca
m
er
a
ca
u
s
e
im
a
g
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
P
erfo
r
ma
n
ce
ev
a
lu
a
tio
n
o
f YOLOv1
1
-
b
a
s
ed
ve
h
icle
d
etec
tio
n
a
n
d
tr
a
ck
in
g
fo
r
u
r
b
a
n
…
(
T
h
a
n
g
C
.
V
u
)
3525
q
u
ality
to
b
e
b
l
u
r
r
ed
(
m
o
tio
n
b
lu
r
)
; U
AV
ch
an
g
es in
altitu
d
e
d
u
r
in
g
o
p
er
atio
n
;
c
h
an
g
es in
lig
h
tin
g
co
n
d
itio
n
s
;
an
d
b
ac
k
g
r
o
u
n
d
m
o
tio
n
o
f
th
e
v
id
eo
af
f
ec
t
th
e
r
esu
lts
.
B
y
d
iv
id
in
g
t
h
e
im
ag
e
in
t
o
o
v
e
r
lap
p
in
g
s
lices,
th
e
SAHI
s
tr
ateg
y
h
elp
s
to
im
p
r
o
v
e
th
e
r
elativ
e
s
ca
le
o
f
o
b
jects
with
in
ea
ch
s
lice.
W
h
ile
th
is
m
eth
o
d
d
o
es
n
o
t
elim
in
ate
th
e
in
f
lu
e
n
ce
o
f
th
e
af
o
r
em
e
n
tio
n
ed
f
ac
to
r
s
,
it
s
o
m
ewh
at
m
in
im
izes
th
e
d
etec
ti
o
n
er
r
o
r
s
r
elate
d
to
s
ca
le
co
m
m
o
n
ly
e
n
co
u
n
ter
ed
i
n
UAV
tr
af
f
ic
m
o
n
ito
r
in
g
.
5.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
an
d
ev
al
u
ates
an
in
teg
r
ate
d
v
eh
icle
d
e
tectio
n
an
d
t
r
ac
k
in
g
f
r
am
ew
o
r
k
b
ased
o
n
YOL
Ov
1
1
f
o
r
I
T
S
a
p
p
licatio
n
s
.
T
h
e
in
c
o
r
p
o
r
atio
n
o
f
th
e
Dee
p
SOR
T
alg
o
r
ith
m
allo
ws
f
o
r
th
e
m
ain
ten
a
n
ce
o
f
v
eh
icle
id
e
n
tific
atio
n
ac
r
o
s
s
co
n
s
ec
u
tiv
e
f
r
am
es,
th
er
eb
y
en
h
an
ci
n
g
th
e
s
tab
ilit
y
an
d
co
n
s
is
ten
cy
o
f
th
e
m
u
lti
-
o
b
ject
tr
ac
k
in
g
s
y
s
tem
.
Simu
ltan
eo
u
s
ly
,
SAHI
tech
n
iq
u
es
ar
e
in
teg
r
ated
to
im
p
r
o
v
e
th
e
d
etec
tio
n
o
f
s
m
all
v
eh
icles
in
r
em
o
te
s
en
s
in
g
im
ag
er
y
an
d
tr
af
f
ic
s
u
r
v
eillan
ce
v
id
eo
d
ata
co
llected
in
T
h
ai
Ng
u
y
en
,
Vietn
am
.
E
x
p
er
im
e
n
tal
r
esu
lts
s
h
o
w
th
at
th
e
s
y
s
tem
ac
h
iev
es
h
ig
h
ef
f
icien
c
y
in
b
o
th
v
eh
icle
d
etec
tio
n
an
d
tr
ac
k
in
g
task
s
th
r
o
u
g
h
ev
al
u
a
tio
n
m
etr
ics
s
u
ch
as
m
A
P,
M
OT
A,
an
d
I
DF1
.
Ho
wev
er
,
s
y
s
tem
p
er
f
o
r
m
a
n
ce
m
ay
s
till
b
e
af
f
ec
ted
in
s
o
m
e
ca
s
es
s
u
ch
as
o
b
ject
o
b
s
cu
r
atio
n
,
m
o
tio
n
b
lu
r
,
o
r
ch
a
n
g
es
in
v
iewin
g
an
g
le.
Fu
tu
r
e
wo
r
k
will
f
o
cu
s
o
n
e
x
ten
d
in
g
t
h
e
ass
ess
m
en
t
to
l
ar
g
er
-
s
ca
le
an
n
o
tated
d
atasets
an
d
in
co
r
p
o
r
atin
g
s
tatis
t
ical
v
alid
atio
n
o
n
lo
n
g
e
r
v
id
e
o
s
eq
u
en
c
es.
Ad
d
itio
n
al
ly
,
in
teg
r
atin
g
m
u
lti
-
s
en
s
o
r
d
ata
s
o
u
r
ce
s
s
u
ch
as
r
ad
ar
o
r
lig
h
t
d
etec
tio
n
a
n
d
r
an
g
in
g
(
L
iDAR
)
,
as
well
as
ex
p
lo
r
in
g
tim
e
-
awa
r
e
lear
n
i
n
g
s
tr
ateg
ies,
is
a
p
r
o
m
is
in
g
d
i
r
ec
tio
n
f
o
r
f
u
r
th
er
im
p
r
o
v
i
n
g
s
u
s
tain
ab
ilit
y
in
co
m
p
lex
u
r
b
an
tr
a
f
f
ic
co
n
d
itio
n
s
.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
e
au
th
o
r
s
wo
u
ld
lik
e
t
o
th
an
k
T
h
ai
Ng
u
y
en
Un
i
v
er
s
ity
o
f
T
ec
h
n
o
lo
g
y
(
T
NUT
)
,
Viet
n
a
m
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
ex
ter
n
al
f
u
n
d
in
g
in
v
o
lv
ed
.
AUTHO
R
CO
NT
RI
B
UT
I
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A
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M
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N
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h
is
jo
u
r
n
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u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT
)
to
r
ec
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g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
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tio
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r
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th
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r
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ip
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tes,
an
d
f
ac
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atio
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.
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DATA AV
AI
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h
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au
th
o
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,
[
MT
N]
,
u
p
o
n
r
eq
u
est.
RE
F
E
R
E
NC
E
S
[
1
]
Y
.
Z
h
a
n
g
,
Z
.
G
u
o
,
J.
W
u
,
Y
.
Ti
a
n
,
H
.
Ta
n
g
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a
n
d
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.
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u
o
,
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v
5
,
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s
t
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b
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v
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l
.
1
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1
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3
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0
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1
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1
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4
.
[
2
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Q
.
L
i
n
,
S
.
Z
h
a
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g
,
a
n
d
S
.
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o
m
p
lete
d
h
is
m
a
ste
r’s
d
e
g
re
e
in
Bio
m
e
d
ica
l
En
g
i
n
e
e
rin
g
fro
m
Ha
n
o
i
Un
i
v
e
rsity
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
l
o
g
y
(HU
S
T
)
in
2
0
1
4
.
He
is
c
u
rre
n
tl
y
p
u
rs
u
in
g
a
P
h
.
D
.
in
El
e
c
tro
n
ics
En
g
i
n
e
e
rin
g
a
t
th
e
Un
iv
e
rsity
o
f
Tec
h
n
o
l
o
g
y
a
n
d
Ed
u
c
a
ti
o
n
(TN
UT).
He
is
a
m
e
m
b
e
r
o
f
th
e
Ad
v
a
n
c
e
d
Wi
re
les
s
Co
m
m
u
n
ica
ti
o
n
Ne
two
r
k
s
(AWCN)
lab
o
ra
to
r
y
d
irec
ted
b
y
Dr.
M
in
h
T.
Ng
u
y
e
n
.
His m
a
in
re
se
a
rc
h
in
tere
sts in
c
lu
d
e
i
n
telli
g
e
n
t
c
o
n
tr
o
l
a
n
d
wire
les
s se
n
so
rs
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
t
d
u
n
g
c
n
d
t@i
c
tu
.
e
d
u
.
v
n
.
As
so
c
.
Pro
f.
Dr
.
Mi
n
h
T.
N
g
u
y
e
n
is
c
u
rre
n
t
ly
th
e
d
irec
to
r
o
f
th
e
Hu
m
a
n
Re
so
u
rc
e
Train
in
g
a
n
d
De
v
e
lo
p
m
e
n
t
Ce
n
ter
a
t
T
h
a
i
Ng
u
y
e
n
U
n
iv
e
rsity
,
Vie
tn
a
m
,
a
n
d
a
lso
th
e
d
irec
to
r
o
f
th
e
Ad
v
a
n
c
e
d
W
irele
ss
Co
m
m
u
n
ica
ti
o
n
Ne
two
rk
s
(AWCN)
lab
.
He
h
a
s
in
tere
st
a
n
d
e
x
p
e
rti
se
in
a
v
a
riety
o
f
re
se
a
rc
h
to
p
ics
in
th
e
c
o
m
m
u
n
ica
ti
o
n
s,
n
e
tw
o
rk
i
n
g
,
a
n
d
sig
n
a
l
p
ro
c
e
ss
in
g
a
re
a
s,
e
sp
e
c
ially
c
o
m
p
re
ss
iv
e
se
n
sin
g
a
n
d
wire
les
s/m
o
b
il
e
se
n
so
r
n
e
two
r
k
s.
He
se
rv
e
s
a
s
a
tec
h
n
ica
l
re
v
iew
e
r
fo
r
se
v
e
ra
l
p
re
stig
io
u
s
j
o
u
r
n
a
ls
a
n
d
in
tern
a
ti
o
n
a
l
c
o
n
fe
re
n
c
e
s.
He
a
lso
se
rv
e
s
a
s
a
n
e
d
i
to
r
fo
r
t
h
e
Wi
re
les
s
Co
m
m
u
n
ica
ti
o
n
a
n
d
M
o
b
il
e
C
o
m
p
u
t
in
g
j
o
u
r
n
a
l
a
n
d
a
n
e
d
it
o
r
-
in
-
c
h
ief
fo
r
ICS
E
S
Tran
sa
c
ti
o
n
s
o
n
Co
m
p
u
ter
Ne
tw
o
rk
s
a
n
d
C
o
m
m
u
n
ica
ti
o
n
s
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
g
u
y
e
n
tu
a
n
m
in
h
@t
n
u
t.
e
d
u
.
v
n
.
Lo
n
g
Q
.
Din
h
is cu
rre
n
tl
y
wo
r
k
in
g
a
s a
lec
tu
re
r
a
t
th
e
De
p
a
rtme
n
t
o
f
En
g
in
e
e
ri
n
g
a
n
d
Tec
h
n
o
l
o
g
y
,
Th
a
i
Ng
u
y
e
n
Un
iv
e
rsity
o
f
In
f
o
rm
a
ti
o
n
a
n
d
Co
m
m
u
n
ica
ti
o
n
Tec
h
n
o
l
o
g
y
(ICTU)
-
Th
a
i
Ng
u
y
e
n
Un
i
v
e
rsit
y
.
He
g
ra
d
u
a
ted
wit
h
a
b
a
c
h
e
lo
r
’s
d
e
g
re
e
in
Au
to
m
a
ti
o
n
i
n
2
0
1
1
fr
o
m
Ha
n
o
i
Un
i
v
e
rsity
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
l
o
g
y
a
n
d
c
o
m
p
lete
d
h
is
m
a
ste
r'
s
d
e
g
re
e
i
n
Co
n
tr
o
l
a
n
d
Au
t
o
m
a
ti
o
n
in
2
0
1
5
fro
m
th
e
sa
m
e
u
n
i
v
e
rsity
.
He
is
c
u
rre
n
tl
y
p
u
rsu
i
n
g
h
is
P
h
.
D
.
in
Co
n
tro
l
a
n
d
Au
t
o
m
a
ti
o
n
E
n
g
i
n
e
e
rin
g
a
t
Th
a
i
N
g
u
y
e
n
U
n
iv
e
rsit
y
o
f
Tec
h
n
o
lo
g
y
-
Vie
tn
a
m
e
se
.
He
is
a
m
e
m
b
e
r
o
f
th
e
Ad
v
a
n
c
e
d
Wi
re
les
s
Co
m
m
u
n
ica
ti
o
n
Ne
two
r
k
s
(AWCN)
lab
o
ra
to
r
y
d
irec
ted
b
y
Dr.
M
i
n
h
T.
Ng
u
y
e
n
.
His
m
a
i
n
re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
in
tell
ig
e
n
t
c
o
n
tro
l
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
d
q
l
o
n
g
@ic
tu
.
e
d
u
.
v
n
.
Mu
i
D.
Ng
u
y
e
n
is
c
u
rre
n
tl
y
w
o
rk
i
n
g
a
s
a
lec
tu
re
r
a
t
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
,
F
a
c
u
lt
y
o
f
I
n
tern
a
ti
o
n
a
l
Train
in
g
,
T
h
a
i
Ng
u
y
e
n
Un
iv
e
rsity
o
f
Tec
h
n
o
l
o
g
y
-
Th
a
i
Ng
u
y
e
n
Un
i
v
e
rsity
.
He
g
ra
d
u
a
ted
fro
m
u
n
iv
e
rsit
y
with
a
m
a
jo
r
in
e
lec
tro
n
ics
a
n
d
tele
c
o
m
m
u
n
ica
ti
o
n
s
i
n
2
0
1
5
a
n
d
c
o
m
p
lete
d
h
is
m
a
ste
r’s
d
e
g
re
e
in
e
lec
tro
n
ic
e
n
g
in
e
e
rin
g
in
2
0
1
8
.
C
u
rre
n
tl
y
,
h
e
is
p
u
rsu
i
n
g
a
P
h
.
D
.
in
C
o
n
tr
o
l
a
n
d
Au
to
m
a
ti
o
n
En
g
in
e
e
rin
g
a
t
th
e
sa
m
e
u
n
i
v
e
rsity
wh
e
re
h
e
h
a
s
b
e
e
n
a
tt
a
c
h
e
d
fo
r
m
a
n
y
y
e
a
rs.
He
is
a
m
e
m
b
e
r
o
f
th
e
Ad
v
a
n
c
e
d
Wi
re
les
s
Co
m
m
u
n
ica
ti
o
n
Ne
tw
o
rk
s
(AWCN)
lab
o
ra
to
r
y
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
d
u
c
m
u
i@t
n
u
t
.
e
d
u
.
v
n
.
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