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Vo
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11
,
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.
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er
2
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4942
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A new
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In
th
is
a
rti
c
le,
a
n
e
w
m
e
th
o
d
o
f
v
e
h
icle
s
d
e
tec
ti
n
g
a
n
d
trac
k
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g
is
p
re
se
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ted
:
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th
re
sh
o
l
d
in
g
fo
ll
o
we
d
b
y
a
m
a
th
e
m
a
ti
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a
l
m
o
rp
h
o
l
o
g
y
trea
tme
n
t
a
re
u
se
d
.
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e
trac
k
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g
p
h
a
se
u
se
s
th
e
in
fo
r
m
a
ti
o
n
a
b
o
u
t
a
v
e
h
icle
.
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o
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i
n
a
l
lab
e
li
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g
is
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ro
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o
se
d
i
n
t
h
is
a
rti
c
le.
It
h
e
l
p
s
to
re
d
u
c
e
so
m
e
a
rtefa
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ts
th
a
t
o
c
c
u
r
a
t
th
e
d
e
tec
ti
o
n
lev
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l.
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h
e
m
a
in
c
o
n
tri
b
u
ti
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o
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h
is
a
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li
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n
t
h
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p
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ss
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it
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o
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m
e
rg
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g
i
n
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rm
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ti
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n
d
h
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le
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a
c
k
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).
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o
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r
wo
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d
s,
it
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sh
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h
a
t
m
a
n
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su
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p
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g
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d
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t
o
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e
i
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rm
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ti
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lab
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g
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h
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d
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m
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s
o
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p
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h
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ey
w
o
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d
s
:
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m
ag
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p
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s
s
in
g
I
n
f
o
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m
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p
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s
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ab
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ac
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Veh
icles d
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tio
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T
h
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s
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o
p
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n
a
c
c
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ss
a
rticle
u
n
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r th
e
CC B
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SA
li
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C
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p
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A
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r
:
Ma
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in
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Natio
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n
s
t
itu
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elec
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m
u
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s
I
C
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POB
1
5
1
8
,
E
l M
n
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ar
,
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s
Sen
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tr
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,
Alg
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m
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am
az
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in
ttic.d
z
1.
I
NT
RO
D
UCT
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N
T
h
e
in
tellig
en
t
tr
an
s
p
o
r
tatio
n
s
y
s
tem
[
1
]
-
[
5
]
ar
e
a
x
is
o
f
g
r
e
at
im
p
o
r
tan
ce
an
d
t
o
p
icality
.
I
t
n
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t
o
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ly
h
elp
s
to
s
m
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th
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f
lo
w
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eh
icles
b
u
t
also
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ce
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th
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u
m
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ac
cid
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I
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icles a
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cr
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p
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6
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1
0
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k
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wn
th
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et
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o
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s
[
1
1
]
b
ased
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a
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ap
p
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o
f
less
co
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itab
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Mo
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1
2
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[
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6
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I
n
[
1
7
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th
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s
p
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a
tex
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Gau
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v
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icles.
T
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d
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in
[
1
8
]
ex
p
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th
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o
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alan
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b
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o
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t
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ak
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ap
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f
ec
tiv
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in
r
ea
l
tim
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is
s
u
e.
W
an
g
et
a
l.
[
1
9
]
u
s
ed
th
e
n
o
tio
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s
o
f
ex
ten
d
e
d
o
p
tical
f
lu
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E
ig
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to
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if
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t
iate
s
tatic
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ar
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f
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o
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m
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v
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g
p
a
r
ts
.
T
h
i
s
m
eth
o
d
h
as
a
s
ig
n
if
ica
n
t
tim
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co
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C
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Z
h
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[
2
0
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ar
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d
esig
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a
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ap
p
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r
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r
o
u
p
s
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f
th
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f
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ll
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win
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p
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:
th
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im
p
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th
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d
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b
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,
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ex
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co
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tiv
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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A
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fo
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tio
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a
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tr
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(
Ma
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4943
T
h
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d
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tr
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in
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p
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a
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[
2
1
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,
[
2
2
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.
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ates
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(
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b
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(
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Fig
u
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1
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a
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2
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1
.
I
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ac
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b
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s
)
.
T
r
ea
tm
en
t
u
s
i
n
g
a
m
o
r
p
h
o
lo
g
ical
tr
an
s
f
o
r
m
atio
n
is
p
er
f
o
r
m
ed
.
As
s
h
o
wn
in
Fig
u
r
e
2
E
ac
h
v
eh
icle
lo
ca
lized
is
r
eg
is
ter
ed
i
n
a
R
ec
t
r
ec
tan
g
le
d
ef
in
ed
b
y
:
x
h
: is th
e
h
ig
h
est p
ix
el
p
o
s
itio
n
o
f
th
e
v
eh
icle.
x
l
: is th
e
lo
west p
ix
el
p
o
s
itio
n
o
f
th
e
v
eh
icle.
y
r
: is th
e
r
ig
h
tm
o
s
t p
ix
el
p
o
s
itio
n
o
f
th
e
v
eh
icle.
yl
: is th
e
lef
tm
o
s
t p
ix
el
p
o
s
itio
n
o
f
th
e
v
e
h
icle.
c
i
, c
j
: a
r
e
th
e
g
eo
m
etr
ic
ce
n
ter
c
o
o
r
d
in
ates o
f
th
e
r
ec
ta
n
g
le
co
n
tain
in
g
th
e
v
eh
icle.
A
v
eh
icle
h
as
b
ee
n
c
h
ar
ac
ter
i
ze
d
b
y
th
is
r
ec
tan
g
le
R
ec
t
an
d
th
e
m
atr
ix
Mg
o
f
g
r
ay
lev
el
(
g
r
ay
lev
el
o
f
th
e
p
ix
el
s
co
n
tain
e
d
in
R
e
ct
).
A
v
eh
icle
d
etec
ted
at
tim
e
t+
1
is
s
ea
r
ch
ed
in
th
e
p
r
ev
io
u
s
im
ag
e
t o
n
a
s
ea
r
ch
ar
ea
[
2
3
]
,
[
2
4
]
d
ef
in
ed
f
r
o
m
th
e
v
eh
icle'
s
g
eo
m
etr
y
ce
n
ter
o
f
th
e
im
ag
e
t+
1.
Fig
u
r
e
2
.
Par
a
m
eter
s
th
at
r
ep
r
esen
t a
v
eh
icle
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
6
,
Dec
em
b
e
r
2
0
2
1
:
4
9
4
2
-
4
9
4
9
4944
W
h
er
e
m
o
r
e
th
an
o
n
e
v
eh
icle
is
in
th
e
s
ea
r
ch
ar
ea
,
we
m
u
s
t
p
r
o
ce
ed
with
th
e
lik
en
h
o
o
d
p
r
o
ce
d
u
r
e
to
en
s
u
r
e
th
e
co
r
r
esp
o
n
d
en
ce
.
No
tin
g
th
at
in
o
u
r
wo
r
k
,
th
e
s
ea
r
ch
ar
ea
is
d
ef
i
n
ed
in
t
h
e
im
a
g
es b
y
th
e
s
izes: (
-
5
,
+
5
)
,
(
-
1
0
,
+1
0
)
a
n
d
(
-
2
0
,
+
2
0
)
f
r
o
m
t
o
p
to
b
o
tto
m
o
f
th
e
im
ag
e.
Fo
r
th
is
,
we
ex
p
lo
it
th
e
h
is
to
g
r
am
s
in
g
r
ay
le
v
el
[
2
5
]
ca
lc
u
lated
f
r
o
m
t
h
e
m
atr
ix
Mg
o
f
ea
c
h
v
eh
icle.
I
n
Fig
u
r
e
3
an
illu
s
tr
ativ
e
ex
am
p
le
is
g
iv
en
.
Ass
u
m
in
g
th
at
we
wan
t
t
o
estab
lis
h
a
co
r
r
esp
o
n
d
e
n
ce
b
et
wee
n
th
e
v
eh
icles
o
f
th
e
m
o
m
en
t
t
+
1
w
h
ich
is
at
th
e
to
p
o
f
th
e
im
ag
e
in
R
ec
t
o
f
Fig
u
r
e
3
(
b
)
an
d
t
h
e
v
eh
icle
s
lo
ca
ted
at
tim
e
t.
Af
ter
in
s
er
tin
g
th
e
s
ea
r
ch
ar
ea
at
tim
e
t
s
ee
Fig
u
r
e
3
(
a)
in
d
is
co
n
tin
u
es
lin
es,
we
f
o
u
n
d
two
v
eh
icles
r
ep
r
esen
ted
b
y
th
ese
g
eo
m
etr
i
c
ce
n
ter
s
th
at
ar
e
in
clu
d
ed
in
th
is
ar
ea
.
T
h
e
n
ex
t
p
h
ase
is
t
h
e
m
o
d
elin
g
o
f
th
e
Mg
g
r
a
y
lev
el
h
is
to
g
r
am
s
f
o
r
th
is
v
eh
icle
an
d
th
e
two
o
th
e
r
s
b
elo
n
g
in
g
to
th
e
s
ea
r
ch
ar
ea
,
th
ese
h
is
to
g
r
am
s
ar
e
u
s
ed
to
ca
lcu
late
th
e
Veh
ic
le
Su
r
f
ac
e
ar
ea
.
Ar
ea
Véhiculei
=
∑
V
i
2
(
k
)
(
1
)
(
a)
(
b
)
Fig
u
r
e
3
.
I
ll
u
s
tr
ativ
e
ex
am
p
le;
(
a)
im
ag
e
o
f
th
e
m
o
m
en
t t
an
d
th
e
g
r
a
y
lev
el
h
is
to
g
r
am
f
o
r
th
e
two
lo
ca
lized
v
eh
icl
es,
(
b
)
im
a
g
e
o
f
th
e
m
o
m
en
t t+
1
an
d
th
e
g
r
ay
lev
el
h
i
s
t
o
g
r
am
f
o
r
th
e
v
e
h
icle
to
f
o
ll
o
w
Fin
ally
,
a
d
ec
is
io
n
is
m
a
d
e,
w
h
ich
is
b
ased
o
n
a
co
m
p
ar
is
o
n
b
etwe
en
th
e
h
is
to
g
r
am
s
u
r
f
ac
es
o
f
t
h
e
two
v
eh
icles
th
at
ar
e
at
tim
e
t
an
d
th
at
o
f
v
eh
icle
o
f
tim
e
t+
1.
T
h
is
d
ec
is
io
n
was
m
a
d
e
b
y
ca
lcu
latin
g
th
e
d
is
tan
ce
s
b
etwe
en
th
ese
s
u
r
f
ac
es,
b
y
s
elec
tin
g
wh
at
h
as th
e
s
m
allest d
is
tan
ce
ac
co
r
d
in
g
to
th
e
f
o
llo
win
g
r
u
le:
Veh
icle
s
elec
ted
=
m
in
d
is
tan
ce
[
(
Ar
ea
VR
,
Ar
ea
V1
)
a
n
d
(
Ar
ea
VR
,
Ar
ea
V2
)
]
(
2)
Ar
ea
VR
:
ar
ea
o
f
a
s
ea
r
c
h
v
eh
icle.
Ar
ea
V1
: a
r
ea
o
f
th
e
f
ir
s
t v
eh
icle
f
o
u
n
d
.
Ar
ea
V2
: a
r
ea
o
f
th
e
s
ec
o
n
d
v
eh
icle
f
o
u
n
d
.
2
.
2
.
I
nfo
rm
a
t
io
n pro
ce
s
s
ing
“
L
a
belin
g
”
Fo
r
ea
ch
v
eh
icle
d
etec
ted
at
tim
e
t,
it
is
attr
ib
u
ted
a
lab
el
d
e
f
in
ed
b
y
a
d
o
u
b
let
(
a
t
,
c
t
)
an
d
(
a
t+
1
,
c
t+
1
)
at
tim
e
t+
1
.
T
h
e
d
etec
tio
n
s
y
s
tem
s
ca
n
s
th
e
im
ag
e
f
r
o
m
to
p
to
b
o
tto
m
a
n
d
f
r
o
m
lef
t
to
r
i
g
h
t.
I
n
t
h
e
im
ag
e
t,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8
7
0
8
A
n
ew me
th
o
d
fo
r
ve
h
icles d
et
ec
tio
n
a
n
d
tr
a
ck
in
g
u
s
in
g
in
fo
r
ma
tio
n
a
n
d
ima
g
e
p
r
o
ce
s
s
in
g
(
Ma
z
o
u
z
i A
min
e
)
4945
th
e
f
ir
s
t
v
e
h
icle
d
etec
ted
a
t
=
1
,
th
e
s
ec
o
n
d
v
eh
icle
d
etec
ted
will
h
av
e
a
t
=
2
.
So
th
e
n
u
m
b
er
a
t
g
i
v
es
th
e
o
r
d
e
r
o
f
ap
p
ea
r
an
ce
o
f
th
e
v
eh
icle
in
th
e
im
ag
e
t.
T
h
e
d
ig
it
c
t
in
d
icate
s
th
e
co
r
r
esp
o
n
d
en
ce
,
th
e
v
e
h
icle
o
f
lab
el
(
a
t
+
1
,
c
t
+
1
)
ap
p
ea
r
ed
at
th
e
a
t+
1
tim
e
in
th
e
im
ag
e
t+
1
an
d
co
r
r
esp
o
n
d
s
to
th
e
v
eh
icle
wh
ich
ap
p
ea
r
ed
at
th
e
c
t+
1
tim
e
in
th
e
p
r
e
v
io
u
s
p
ictu
r
e
t.
I
n
Fig
u
r
e
4
(
a)
,
we
h
av
e
two
im
ag
es
t,
wh
ich
r
e
p
r
esen
t
an
e
m
p
ty
r
o
a
d
an
d
a
r
o
ad
h
as
th
r
e
e
v
eh
icles
ca
r
r
y
in
g
th
e
lab
els
(
1
,
1
)
,
(
2
,
2
)
an
d
(
3
,
3
)
.
W
h
ile
in
Fig
u
r
e
4
(
b
)
,
we
ca
n
s
ee
two
im
ag
es
t+
1
,
wh
ich
r
ep
r
esen
t
a
r
o
ad
to
a
v
e
h
icle
lab
eled
b
y
(
1
,
0
)
an
d
a
n
o
th
e
r
to
th
r
ee
v
eh
icl
es lab
eled
b
y
(
1
,
1
)
,
(
2
,
2
)
a
n
d
(
3
.
3
)
.
I
n
th
e
im
a
g
es
t+
1
,
th
e
lab
el
(
1
,
0
)
in
d
icate
s
t
h
at
th
er
e
is
a
v
e
h
icle
th
at
h
as
j
u
s
t
ap
p
ea
r
e
d
as
a
n
ew
o
b
ject
a
n
d
th
at
th
is
v
eh
icle
d
o
es
n
o
t
ap
p
ea
r
in
th
e
i
m
ag
e
t
,
wh
ile
th
e
p
air
s
(
1
,
1
)
,
(
2
,
2
)
a
n
d
(
3
,
3
)
m
ea
n
th
at
t
h
er
e
a
r
e
th
r
ee
v
eh
icles
in
th
e
im
ag
e
t+
1
an
d
th
at
it is
v
eh
icl
es lo
o
k
s
lik
e
th
e
s
am
e
v
e
h
icle
s
lab
eled
b
y
(
1
,
1
)
,
(
2
,
2
)
a
n
d
(
3
,
3
)
.
(
a)
(
b
)
Fig
u
r
e
4
.
Prin
cip
le
o
f
lab
els
;
(
a)
im
ag
es t,
(
b
)
im
ag
e
t+
1
2
.
3
.
Dis
cus
s
io
n a
nd
a
s
ce
rt
a
inm
ent
I
n
th
e
p
r
ev
io
u
s
s
ec
tio
n
,
we
d
i
s
cu
s
s
ed
th
e
b
asic
p
r
in
cip
le
o
f
lab
elin
g
.
No
w
we
wan
t
to
d
e
v
elo
p
th
is
p
r
in
cip
le.
A
s
im
p
le
an
al
y
s
is
s
h
o
ws th
at
f
o
r
a
g
iv
en
im
ag
e:
a.
Ma
x
(
a
t
)
is
th
e
to
tal
n
u
m
b
er
o
f
v
eh
icles d
etec
ted
at
tim
e
t.
b.
I
f
at
tim
e
t+
1
,
we
o
b
s
er
v
e
t
h
a
t
o
n
e
o
f
th
e
v
eh
icles
ca
r
r
ies
a
lab
el
c
t+
1
=
0
,
th
en
t
h
e
n
u
m
b
er
o
f
v
eh
icles
at
tim
e
t+
1
b
ec
o
m
es M
ax
(
a
t
)
+1
.
c.
I
f
m
ax
(
a
t
+
1
)
=m
ax
(a
t
)
-
1
,
t
h
en
t
h
er
e
is
a
d
is
ap
p
ea
r
a
n
ce
o
f
a
v
e
h
icle
in
th
e
im
ag
e
t+
1.
Sin
ce
th
e
lo
w
lev
el
o
f
p
r
o
ce
s
s
in
g
,
wh
ich
is
p
ar
ticu
lar
ly
b
ased
o
n
th
r
esh
o
ld
in
g
an
d
m
a
th
em
atica
l
m
o
r
p
h
o
lo
g
y
,
g
en
e
r
ates
a
lo
t
o
f
er
r
o
r
s
,
th
ese
ca
u
s
e
s
:
th
e
d
u
p
licatio
n
o
f
o
b
jects,
th
e
b
ad
ap
p
ea
r
a
n
ce
,
an
d
co
n
s
eq
u
en
tly
r
ef
lects
o
n
t
h
e
r
u
les
d
escr
ib
ed
ab
o
v
e.
Fo
r
ex
a
m
p
le,
if
th
e
s
am
e
v
eh
icle
o
f
t
h
e
in
s
tan
t
t
will
b
e
s
u
b
d
iv
id
ed
i
n
to
two
o
b
jects
at
th
e
in
s
tan
t
t+
1
,
in
th
is
ca
s
e
we
ca
n
ar
r
iv
e
in
s
itu
atio
n
s
wh
er
e
th
ese
two
o
b
jects
will g
en
er
ate
a
p
r
o
b
lem
o
f
in
c
r
ea
s
e
o
f
v
eh
icles n
u
m
b
er
at
ti
m
e
t+
1.
An
o
th
er
v
er
y
im
p
o
r
ta
n
t
ex
am
p
le
is
th
at
o
n
e
ca
n
h
av
e
a
s
itu
atio
n
wh
er
e
a
v
eh
icle
alr
ea
d
y
ap
p
ea
r
ed
,
will
b
e
d
etec
ted
as
a
n
ew
o
b
j
ec
t.
I
n
o
r
d
er
to
r
is
e
to
th
ese
p
r
o
b
lem
s
,
a
co
r
r
ec
tio
n
ap
p
r
o
a
ch
th
at
is
b
ased
o
n
r
u
les th
at
ca
n
b
e
s
u
m
m
ar
ized
b
y
th
e
f
o
llo
win
g
p
o
in
ts
:
a.
T
h
e
p
h
en
o
m
en
o
n
o
f
a
p
p
ea
r
a
n
ce
is
o
n
ly
f
o
r
a
n
ew
v
eh
icle
th
at
is
in
th
e
ar
ea
o
f
ap
p
ea
r
an
ce
(
th
e
ar
ea
f
ar
th
est f
r
o
m
t
h
e
ca
m
er
a)
.
b.
On
th
e
o
th
er
h
a
n
d
,
th
e
p
h
en
o
m
e
n
o
n
o
f
d
is
ap
p
ea
r
a
n
ce
is
d
ef
in
ed
if
a
v
eh
icle
o
f
m
o
m
e
n
t
t
ap
p
ea
r
s
in
th
e
zo
n
e
o
f
d
is
ap
p
ea
r
a
n
ce
(
th
e
z
o
n
e
clo
s
est to
th
e
ca
m
er
a)
a
n
d
t
h
at
th
is
o
b
ject
d
o
es n
o
t a
p
p
ea
r
at
tim
e
t+
1.
I
n
th
is
ar
ticle,
we
co
n
s
id
er
e
d
t
h
e
s
im
p
le
ca
s
e
o
f
a
o
n
e
-
way
r
o
ad
(
f
r
o
m
to
p
to
b
o
tto
m
o
r
f
r
o
m
r
ig
h
t
to
lef
t
)
.
W
e
p
er
f
o
r
m
ed
d
u
r
in
g
an
ap
p
ea
r
a
n
ce
o
f
a
v
e
h
icle
th
e
v
alu
e
0
to
c
.
T
h
e
ad
d
itio
n
o
f
t
h
ese
r
u
les
allo
ws
a
clea
r
im
p
r
o
v
e
m
en
t
f
o
r
o
u
r
d
e
tectio
n
/
tr
ac
k
in
g
m
eth
o
d
.
O
u
r
ap
p
r
o
ac
h
is
to
ap
p
ly
a
c
o
r
r
ec
t
io
n
p
r
o
ce
d
u
r
e
,
it
is
g
iv
en
b
y
th
e
f
o
l
lo
win
g
alg
o
r
it
h
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
6
,
Dec
em
b
e
r
2
0
2
1
:
4
9
4
2
-
4
9
4
9
4946
B
eg
in
a.
R
ea
d
o
f
im
ag
e.
b.
An
en
lar
g
em
en
t
o
f
th
e
s
ea
r
ch
ar
ea
b
u
ilt
ar
o
u
n
d
v
eh
icles
with
lab
ellin
g
er
r
o
r
s
(
c
t
=
0
an
d
th
i
s
v
eh
icle
d
o
es
n
o
t b
elo
n
g
to
t
h
e
ar
ea
o
f
ap
p
e
ar
an
ce
)
.
c.
R
ep
ea
t th
e
o
p
er
atio
n
o
f
th
e
ter
r
ain
g
eo
m
et
r
y
an
d
th
e
m
a
p
p
in
g
.
d.
Ass
ig
n
lab
els f
o
r
v
eh
icles with
lab
ellin
g
er
r
o
r
s
.
e.
Gr
o
u
p
o
b
jects th
at
h
av
e
t
h
e
s
am
e
lab
el
an
d
(
if
th
ey
ex
is
t)
in
th
e
s
am
e
o
b
ject.
f.
T
est
th
e
lab
els
o
f
th
e
v
eh
icles
ag
ain
an
d
r
ed
o
t
h
e
ab
o
v
em
en
tio
n
ed
s
p
o
ts
f
o
r
t
h
e
ca
s
e
o
f
in
co
r
r
ec
t
"lab
e
l
er
r
o
r
"
d
etec
tio
n
s
.
g.
Sto
p
as so
o
n
as y
o
u
g
et
a
g
o
o
d
d
etec
tio
n
.
E
n
d
3.
E
VA
L
UA
T
I
O
N
O
F
O
UR
M
E
T
H
O
D
AND
R
E
S
UL
T
S
I
f
we
tr
y
to
m
a
k
e
a
n
e
v
alu
atio
n
o
f
o
u
r
ap
p
r
o
ac
h
co
m
p
ar
ed
t
o
th
e
ex
is
tin
g
w
o
r
k
s
,
we
ca
n
n
o
tice
th
at
th
is
ap
p
r
o
ac
h
ca
n
b
e
ap
p
lied
i
n
r
ea
l
tim
e
co
m
p
ar
e
d
to
th
e
d
if
f
er
en
t
m
eth
o
d
s
wh
ich
ar
e
b
ased
o
n
th
e
a
r
tific
ial
in
tellig
en
ce
[
2
6
]
,
[
2
7
].
I
n
ad
d
i
tio
n
,
o
u
r
m
eth
o
d
is
b
ased
o
n
a
p
r
in
cip
le
o
f
f
u
s
io
n
b
etwe
en
t
h
e
asp
ec
t
o
f
im
a
g
e
p
r
o
ce
s
s
in
g
an
d
in
f
o
r
m
atio
n
p
r
o
ce
s
s
in
g
'
lab
els
'
wh
ich
g
iv
es
a
n
ad
v
a
n
tag
e
o
v
er
co
n
v
en
tio
n
al
m
et
h
o
d
s
b
ased
o
n
th
e
asp
ec
t o
f
im
ag
e
p
r
o
ce
s
s
in
g
[
2
8
]
,
[
2
9
].
I
n
th
e
Fig
u
r
e
5
th
at
f
o
llo
ws,
f
o
u
r
ty
p
es
o
f
v
i
d
eo
s
eq
u
e
n
ce
s
:
two
in
r
o
ad
(
th
e
tr
a
f
f
ic
f
lo
w
f
r
o
m
to
p
t
o
b
o
tto
m
o
f
im
ag
e)
,
th
e
th
ir
d
o
n
e
in
s
id
e
a
tu
n
n
el
a
n
d
th
e
last
o
n
e
is
in
r
o
ad
(
th
e
tr
af
f
ic
f
lo
w
f
r
o
m
r
ig
h
t
to
lef
t
o
f
im
ag
e)
.
W
e
ca
n
o
b
s
er
v
e
a
cle
ar
im
p
r
o
v
em
en
t
o
f
th
e
d
ete
ctio
n
/
tr
ac
k
in
g
p
r
o
c
ed
u
r
e
an
d
th
is
b
ec
au
s
e
o
f
th
e
im
p
lem
en
tatio
n
o
f
th
e
co
r
r
ec
ti
o
n
p
r
o
ce
d
u
r
e.
I
n
th
ese
ex
am
p
l
es,
we
h
av
e
tr
ied
to
co
r
r
ec
t sev
er
al
er
r
o
r
s
,
am
o
n
g
th
ese
co
r
r
ec
tio
n
s
th
e
g
r
o
u
p
in
g
o
f
o
b
jects th
at
ca
r
r
y
th
e
s
am
e
lab
els an
d
th
e
d
eletio
n
o
f
o
b
je
cts th
at
h
av
e
lab
els
o
f
c
t
=
0
,
an
d
wh
ich
ar
e
o
u
ts
id
e
th
e
ar
ea
o
f
ap
p
ea
r
an
ce
.
T
h
e
m
o
d
if
icatio
n
o
f
t
h
e
o
b
jects
lab
els
wh
ich
ca
r
r
y
lab
els c
t
=
0
an
d
wh
ich
ar
e
o
u
ts
id
e
th
e
zo
n
e
o
f
a
p
p
ea
r
a
n
ce
.
(a
)
(b
)
Fig
u
r
e
5
.
D
etec
tio
n
/tra
c
k
in
g
r
esu
lts
;
(
a)
with
o
u
t th
e
co
r
r
ec
tio
n
p
h
ase,
(
b
)
with
t
h
e
co
r
r
ec
tio
n
p
h
ase
(
c
o
n
tin
u
e)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8
7
0
8
A
n
ew me
th
o
d
fo
r
ve
h
icles d
et
ec
tio
n
a
n
d
tr
a
ck
in
g
u
s
in
g
in
fo
r
ma
tio
n
a
n
d
ima
g
e
p
r
o
ce
s
s
in
g
(
Ma
z
o
u
z
i A
min
e
)
4947
(
a)
(
b
)
Fig
u
r
e
5
.
D
etec
tio
n
/tra
c
k
in
g
r
esu
lts
;
(
a)
with
o
u
t th
e
co
r
r
ec
tio
n
p
h
ase,
(
b
)
with
t
h
e
co
r
r
ec
tio
n
p
h
ase
4.
CO
NCLU
SI
O
N
I
n
th
is
a
r
ticle,
a
m
eth
o
d
b
ased
o
n
two
le
v
els
o
f
p
r
o
c
ess
in
g
,
n
am
ely
im
ag
e
p
r
o
ce
s
s
in
g
an
d
in
f
o
r
m
atio
n
p
r
o
ce
s
s
in
g
,
h
as
b
ee
n
p
r
esen
ted
to
d
etec
t
an
d
tr
ac
k
v
eh
icles
in
v
id
e
o
s
eq
u
en
c
es.
Ou
r
co
n
tr
ib
u
tio
n
is
to
ad
d
a
co
r
r
ec
tio
n
lo
o
p
b
y
ex
p
lo
itin
g
t
h
e
lev
el
o
f
in
f
o
r
m
atio
n
p
r
o
ce
s
s
in
g
w
h
ich
is
b
ased
m
ain
ly
o
n
th
e
p
r
in
cip
le
o
f
la
b
elin
g
,
th
e
latt
er
s
h
o
wed
its
ef
f
ec
tiv
en
ess
in
o
r
d
e
r
to
en
h
a
n
ce
th
e
r
esu
l
ts
o
f
th
e
v
eh
icles
d
etec
tio
n
esp
e
cially
in
th
e
ca
s
e
o
f
th
e
s
u
b
d
i
v
is
io
n
o
f
o
b
jects
,
an
d
in
th
e
p
r
esen
ce
o
f
a
r
tifa
c
ts
.
T
h
e
p
er
s
p
ec
tiv
e
wo
r
k
is
co
n
ce
n
t
r
ed
ar
o
u
n
d
th
e
tr
af
f
ic
r
o
a
d
m
o
d
eliza
tio
n
,
u
s
i
n
g
a
tr
an
s
f
e
r
t m
atr
ix
an
d
th
e
la
b
els m
an
ag
em
en
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
6
,
Dec
em
b
e
r
2
0
2
1
:
4
9
4
2
-
4
9
4
9
4948
RE
F
E
R
E
NC
E
S
[1
]
K.
P
o
o
ra
n
i
,
A.
S
h
a
rm
il
a
,
a
n
d
G
.
S
u
j
it
h
a
ra
,
“
IOT
Ba
se
d
li
v
e
stre
a
m
in
g
of
v
e
h
icle
,
p
o
siti
o
n
a
c
c
id
e
n
t
p
re
v
e
n
ti
o
n
a
n
d
d
e
tec
ti
o
n
sy
ste
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Rec
e
n
t
T
re
n
d
s
i
n
En
g
i
n
e
e
rin
g
&
Res
e
a
rc
h
(IJ
RT
ER
)
,
v
o
l.
3
,
p
p
.
5
2
-
5
5
,
2
0
1
7
.
[2
]
A
.
J
.
S
a
m
u
e
l
a
n
d
S
.
S
e
b
a
stian
,
“
An
a
lg
o
ri
th
m
fo
r
Io
T
b
a
se
d
v
e
h
ic
le
v
e
rifi
c
a
ti
o
n
s
y
ste
m
u
sin
g
RF
ID
,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(IJ
ECE
)
,
v
o
l.
9
,
n
o
.
5
,
p
p
.
3
7
5
1
-
3
7
5
8
,
2
0
1
9
,
d
o
i:
1
0
.
1
1
5
9
1
/i
jec
e
.
v
9
i5
.
p
p
3
7
5
1
-
3
7
5
8
.
[3
]
A
.
M
a
z
o
u
z
i
a
n
d
M
.
F
.
Be
l
B
a
c
h
ir,
“
En
h
a
n
c
e
m
e
n
t
o
f
t
h
e
D
e
tec
ti
o
n
f
o
r
I
n
telli
g
e
n
t
Ve
h
icle
S
y
ste
m
s
-
Ca
se
Ra
in
/S
n
o
w
,”
In
ter
n
a
ti
o
n
a
l
Rev
iew
o
f
A
u
to
m
a
ti
c
C
o
n
tro
l
(IR
EA
CO
),
v
o
l.
1
0
,
n
o
.
2
,
2
0
1
7
,
d
o
i
:
1
0
.
1
5
8
6
6
/i
re
a
c
o
.
v
1
0
i2
.
8
2
4
2
.
[4
]
M
.
Am
in
e
a
n
d
K
.
Dj
o
u
d
i,
“
Ve
h
ic
les
d
e
tec
ti
o
n
u
si
n
g
t
h
e
M
LP
a
n
d
t
h
e
c
o
rre
latio
n
m
e
a
su
re
m
e
n
t,
”
2
0
1
9
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ad
v
a
n
c
e
d
El
e
c
trica
l
En
g
in
e
e
rin
g
(ICAE
E)
,
2
0
1
9
,
p
p
.
1
-
5
,
d
o
i:
1
0
.
1
1
0
9
/ICAE
E4
7
1
2
3
.
2
0
1
9
.
9
0
1
5
1
4
4
.
[5
]
V
.
S
.
P
a
d
il
la,
R
.
A.
P
o
n
g
u
il
lo
,
A
.
A.
A
b
a
d
,
a
n
d
L
.
E.
S
a
las
,
“
Cy
b
e
r
-
p
h
y
sic
a
l
sy
ste
m
b
a
se
d
o
n
im
a
g
e
re
c
o
g
n
i
ti
o
n
to
imp
ro
v
e
traffic
flo
w
A
c
a
se
stu
d
y
,
”
I
n
ter
n
a
t
io
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
E
n
g
in
e
e
rin
g
(IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
5
,
p
p
.
5
2
1
7
-
5
2
2
6
,
2
0
2
0
,
d
o
i:
1
0
.
1
1
5
9
1
/
ij
e
c
e
.
v
1
0
i5
.
p
p
5
2
1
7
-
5
2
2
6
.
[6
]
P
.
A.
Targ
e
a
n
d
M
.
P
.
S
a
to
n
e
,
“
VA
NET
b
a
se
d
Re
a
l
-
Ti
m
e
In
telli
g
e
n
t
Tran
sp
o
rtatio
n
S
y
ste
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
A
p
p
l
ica
ti
o
n
s,
v
o
l.
1
4
5
,
n
o
.
4
,
p
p
.
3
4
-
3
8
,
2
0
1
6
,
d
o
i:
1
0
.
5
1
2
0
/i
jca
2
0
1
6
9
1
0
5
8
2
.
[7
]
H
.
Da
h
h
o
u
e
t
a
l
.
,
“
De
sig
n
a
n
d
Im
p
lem
e
n
tatio
n
In
telli
g
e
n
t
Ad
a
p
ti
v
e
F
ro
n
t
-
li
g
h
ti
n
g
S
y
ste
m
o
f
Au
to
m
o
b
i
le
u
sin
g
Dig
it
a
l
Tec
h
n
o
l
o
g
y
o
n
Ard
u
i
n
o
b
o
a
rd
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
En
g
in
e
e
rin
g
(IJ
ECE
)
,
v
o
l.
8
,
n
o
.
1
,
p
p
.
5
2
1
-
5
2
9
,
d
o
i:
1
0
.
1
1
5
9
1
/i
jec
e
.
v
8
i1
.
p
p
5
2
1
-
5
2
9
.
[8
]
H.
M
.
Ali
a
n
d
Z.
S
.
Alwa
n
,
“
Ca
r
Ac
c
id
e
n
t
De
tec
ti
o
n
a
n
d
No
ti
fic
a
ti
o
n
S
y
ste
m
Us
in
g
S
m
a
rtp
h
o
n
e
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
M
o
b
il
e
C
o
mp
u
t
in
g
,
v
o
l
.
4
,
n
o
.
4
,
p
p
.
6
2
0
-
6
3
5
,
2
0
1
5
.
[9
]
I
.
Ah
m
a
d
,
R
.
M
d
.
No
o
r
,
A
.
Ih
sa
n
,
a
n
d
M
.
A
.
Qu
re
sh
i
,
“
Th
e
R
o
l
e
o
f
Ve
h
icu
lar
Cl
o
u
d
C
o
m
p
u
ti
n
g
in
Ro
a
d
Traffic
M
a
n
a
g
e
m
e
n
t:
A
S
u
r
v
e
y
,
”
I
n
ter
n
a
ti
o
n
a
l
c
o
n
fer
e
n
c
e
o
n
fu
t
u
re
i
n
telli
g
e
n
t
v
e
h
ic
u
la
r
tec
h
n
o
lo
g
ies
,
2
0
1
7
,
p
p
.
1
2
3
-
1
3
1
,
d
o
i:
1
0
.
1
0
0
7
/9
7
8
-
3
-
3
1
9
-
5
1
2
0
7
-
5
_
1
2
[1
0
]
M.
D
.
Ku
m
a
r
HSDK,
S.
G
u
p
ta
,
S.
Ku
m
a
r,
a
n
d
S
.
S
riv
a
sta
v
a
,
“
Ac
c
id
e
n
t
De
tec
ti
o
n
a
n
d
Re
p
o
rti
n
g
S
y
ste
m
Us
in
g
G
P
S
a
n
d
G
S
M
M
o
d
u
le,”
J
o
u
rn
a
l
o
f
Eme
rg
in
g
T
e
c
h
n
o
l
o
g
ies
a
n
d
In
n
o
v
a
ti
v
e
Res
e
a
rc
h
(J
ET
IR)
,
v
o
l.
2
,
n
o
.
5
,
p
p
.
1
4
3
3
-
1
4
3
6
,
2
0
1
5
.
[1
1
]
D
.
Ke
rfa
a
n
d
M
.
F
.
Be
l
b
a
c
h
ir,
“
An
Eff
icie
n
t
Re
a
l
T
ime
M
o
v
in
g
Ob
jec
t
De
tec
ti
o
n
S
c
h
e
m
e
Us
in
g
Dia
m
o
n
d
S
e
a
rc
h
Alg
o
rit
h
m
a
n
d
M
a
t
h
e
m
a
ti
c
a
l
M
o
rp
h
o
lo
g
y
,
”
In
ter
n
a
ti
o
n
a
l
Rev
iew
o
n
C
o
mp
u
ter
s
a
n
d
S
o
ft
wa
re
(I
R
ECOS
),
vol
.
9
,
n
o
.
0
5
,
2
0
1
4
.
[1
2
]
Y.
Iv
a
n
o
v
,
A.
Bo
b
ick
,
a
n
d
J.
Li
u
,
“
F
a
st
li
g
h
ti
n
g
in
d
e
p
e
n
d
e
n
t
b
a
c
k
g
ro
u
n
d
s
u
b
trac
ti
o
n
,
”
Pro
c
e
e
d
i
n
g
s
1
9
9
8
IEE
E
W
o
rk
sh
o
p
o
n
Vi
su
a
l
S
u
rv
e
il
la
n
c
e
,
1
9
9
8
,
p
p
.
4
9
-
55
,
d
o
i:
1
0
.
1
1
0
9
/W
VS.
1
9
9
8
.
6
4
6
0
2
0
.
[1
3
]
P
.
Bh
a
sk
a
r
a
n
d
S
.
P
.
Yo
n
g
,
“
ima
g
e
p
r
o
c
e
s
s
i
n
g
b
a
s
e
d
v
e
h
i
c
l
e
d
e
t
e
c
t
i
o
n
a
n
d
t
r
a
c
k
i
n
g
m
e
t
h
o
d
,
”
2
0
1
4
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
C
o
m
p
u
t
e
r
a
n
d
I
n
f
o
r
m
a
t
i
o
n
S
c
i
e
n
c
e
s
(
I
C
C
O
I
N
S
)
,
2014
,
p
p
.
1
-
5
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
C
O
I
N
S
.
2
0
1
4
.
6
8
6
8
3
5
7
.
[1
4
]
S.
G
u
p
te,
O.
M
a
so
u
d
,
R
.
F
.
K.
M
a
rti
n
,
a
n
d
N
.
P
.
P
a
p
a
n
ik
o
lo
p
o
u
lo
s,
“
De
tec
ti
o
n
a
n
d
Clas
sifica
ti
o
n
o
f
Ve
h
icle
s
,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
In
telli
g
e
n
t
T
ra
n
sp
o
rta
ti
o
n
S
y
ste
ms
,
v
o
l.
3
,
n
o
.
1,
pp
.
3
7
-
47
,
2
0
0
2
,
d
o
i:
1
0
.
1
1
0
9
/6
9
7
9
.
9
9
4
7
9
4
.
[1
5
]
B.
P
a
wa
r
,
V.
T.
Hu
m
b
e
,
a
n
d
L
.
Ku
n
d
n
a
n
i
,
“
M
o
r
p
h
o
lo
g
y
b
a
se
d
m
o
v
in
g
v
e
h
icle
d
e
tec
ti
o
n
,
”
2
0
1
7
In
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
Bi
g
Da
t
a
An
a
lytics
a
n
d
Co
m
p
u
t
a
ti
o
n
a
l
In
tel
li
g
e
n
c
e
(ICBDA
C)
,
2
0
1
7
,
p
p
.
2
1
7
-
2
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/ICBDACI.
2
0
1
7
.
8
0
7
0
8
3
7
.
[1
6
]
B.
Alsh
a
q
a
q
i,
M.
B
o
u
m
e
h
e
d
,
A.
Ou
a
m
ri,
a
n
d
M.
Ke
c
h
e
,
“
Im
p
lem
e
n
tatio
n
o
f
d
ista
n
c
e
a
n
d
sp
e
e
d
m
e
a
su
re
m
e
n
talg
o
rit
h
m
s
fo
r
t
h
e
d
e
v
e
lo
p
m
e
n
t
o
f
a
n
a
u
to
m
a
ti
c
traff
ic
r
e
g
u
lati
o
n
s
y
ste
m
,
”
In
ter
n
a
t
io
n
a
l
Rev
iew
o
n
Co
mp
u
ter
s
a
n
d
S
o
ft
w
a
re
(IR
ECO
S
),
v
o
l.
7
,
n
o
.
6
,
p
p
.
2
8
0
4
-
2
8
0
9
,
2
0
1
2
.
[1
7
]
C.
S
tau
ffe
r
a
n
d
W.
E.
L.
G
rims
o
n
,
“
Ad
a
p
ti
v
e
b
a
c
k
g
r
o
u
n
d
m
i
x
tu
re
m
o
d
e
ls
fo
r
re
a
l
-
ti
m
e
trac
k
in
g
,
”
Pro
c
e
e
d
in
g
s
.
1
9
9
9
IEE
E
c
o
mp
u
ter
so
c
iety
c
o
n
fer
e
n
c
e
o
n
c
o
mp
u
ter
v
isio
n
a
n
d
p
a
tt
e
rn
re
c
o
g
n
it
i
o
n
(C
a
t.
N
o
PR
0
0
1
4
9
),
v
o
l.
2
,
1
9
9
9
,
p
p
.
2
4
6
-
2
5
2
,
d
o
i:
1
0
.
1
1
0
9
/
CVPR.
1
9
9
9
.
7
8
4
6
3
7
.
[1
8
]
M
.
Tsu
c
h
ik
a
wa
,
A.
S
a
t
o
,
H.
Ko
ik
e
,
a
n
d
A.
T
o
m
o
n
o
,
“
A
m
o
v
i
n
g
-
o
b
jec
t
e
x
trac
ti
o
n
m
e
th
o
d
ro
b
u
st
a
g
a
in
st
il
lu
m
in
a
ti
o
n
lev
e
l
c
h
a
n
g
e
s
fo
r
a
p
e
d
e
stria
n
c
o
u
n
ti
n
g
sy
ste
m
,
”
Pro
c
e
e
d
in
g
s
o
f
In
ter
n
a
ti
o
n
a
l
S
y
mp
o
si
u
m
o
n
Co
mp
u
ter
V
isio
n
-
I
S
CV
,
1
9
9
5
,
p
p
.
5
6
3
-
5
6
8
,
d
o
i:
1
0
.
1
1
0
9
/I
S
CV.1
9
9
5
.
4
7
7
0
6
1
.
[1
9
]
J
.
Wan
g
,
G
.
Be
b
is
,
a
n
d
R
.
M
il
le
r,
“
Ov
e
rtak
i
n
g
Ve
h
icle
De
tec
ti
o
n
Us
in
g
Dy
n
a
m
ic
a
n
d
Q
u
a
si
-
S
ta
ti
c
Ba
c
k
g
r
o
u
n
d
M
o
d
e
li
n
g
,”
2
0
0
5
IEE
E
Co
m
p
u
te
r
S
o
c
iety
C
o
n
fer
e
n
c
e
o
n
C
o
mp
u
t
e
r
Vi
sio
n
a
n
d
Pa
t
ter
n
Rec
o
g
n
it
i
o
n
(CVP
R'
0
5
)
-
W
o
rk
sh
o
p
s
,
2
0
0
5
,
p
p
.
6
4
-
6
4
,
d
o
i:
1
0
.
1
1
0
9
/CVP
R
.
2
0
0
5
.
5
0
6
.
[2
0
]
C.
Ch
e
n
a
n
d
X.
Z
h
a
n
g
,
“
M
o
v
in
g
Ve
h
icle
De
tec
ti
o
n
Ba
se
d
o
n
Un
io
n
o
f
Th
re
e
-
F
ra
m
e
Diffe
re
n
c
e
,
”
Ad
v
a
n
c
e
s
in
El
e
c
tro
n
ic
En
g
in
e
e
rin
g
,
Co
mm
u
n
ica
ti
o
n
a
n
d
M
a
n
a
g
e
me
n
t
v
o
l.
2,
v
o
l.
1
4
0
,
2
0
1
2
,
p
p
.
4
5
9
-
4
6
4
,
d
o
i:
1
0
.
1
0
0
7
/9
7
8
-
3
-
642
-
2
7
2
9
6
-
7
_
7
1
.
[2
1
]
V.
Re
k
h
a
a
a
n
d
K
.
Na
tara
jan
,
“
F
o
re
g
r
o
u
n
d
a
lg
o
rit
h
m
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ti
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.
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