TELK
OMNIKA
Indonesian
Journal
of
Electrical
Engineering
V
ol.
12,
No
.
11,
No
v
ember
2014,
pp
.
7778
7784
DOI:
10.11591/telk
omnika.v12i11.6545
7778
W
eighted
Samples
Based
Bac
kgr
ound
Modeling
f
or
the
T
ask
of
Motion
Detection
in
Video
Sequences
Boubekeur
Mohamed
Bac
hir
*1
,
Benlefki
T
arek
2
,
and
Luo
SenLin
,
Labidi
Hocine
1
1
School
of
Inf
or
mation
and
Electronics
,
Beijing
Institute
of
T
echnology
1
Beijing,
China
100081
2
School
of
Electronics
and
Inf
or
mation
Engineer
ing,
Beihang
Univ
ersity
Beijing,
China
100191
*1
corresponding
author
,
e-mail:
msboubek
eur@y
ahoo
.fr
Abstract
In
this
paper
,
a
non
par
ametr
ic
method
f
or
bac
kg
round
subtr
action
and
mo
ving
object
detection
based
on
adaptiv
e
threshold
using
successiv
e
squared
diff
erences
and
including
fr
ame
diff
erence
process
is
proposed.
the
presented
scheme
f
ocused
on
the
case
of
adaptiv
e
threshold
and
dependent
distance
calculation
using
a
w
eighted
estimation
procedure
.
In
contr
ast
with
the
e
xisting
update
procedures
(First-
in
First-out,
r
andom
pic
kup),
W
e
proposed
an
intuitiv
e
update
policy
to
the
bac
kg
round
model
based
on
associated
decreasing
w
eights
.
The
presented
algor
ithm
succeeds
on
e
xtr
acting
the
mo
ving
f
oreg
round
with
efficiency
and
o
v
er
passes
the
prob
lematic
of
ghost
situations
.
The
proposed
fr
ame
w
or
k
pro
vides
rob
ustness
to
noise
.
Exper
iments
sho
w
competitiv
e
results
compared
to
e
xisting
approaches
and
demonstr
ate
the
applicability
of
the
proposed
scheme
in
a
v
ar
iety
of
video
sur
v
eillance
scenar
ios
.
K
e
yw
or
ds:
Bac
kg
round
Subtr
action,
sur
v
eillance
,
w
eighted
samples
.
Cop
yright
c
2014
Institute
of
Ad
v
anced
Engineering
and
Science
.
All
rights
reser
v
ed.
1.
Intr
oduction
Most
of
static
camer
a
based
monitor
ing
systems
f
or
secur
ity
pur
poses
rely
on
bac
kg
round
modeling
and
subtr
action
process
f
or
detecting
and
identifying
mo
ving
f
ore
g
round
objects
in
the
video
scene
,
the
main
adv
antage
of
bac
kg
round
subtr
action
techniques
is
that
no
pr
ior
kno
wledge
on
the
nature
of
the
target
object
to
be
detected
is
needed.
The
subtr
action
of
inconsistent
in-
f
or
mation
e
xisting
in
the
bac
kg
round
implies
the
retr
ie
v
al
of
interesting
f
oreg
round
objects
.
One
ma
y
easily
v
er
ify
the
spatial
consistency
betw
een
neighbor
ing
pix
els
resulting
of
a
high
correlation
betw
een
the
intensity
v
alues
in
a
tight
neighborhood.
The
tempor
al
inf
or
mation
pro
vided
b
y
the
succession
of
fr
ames
is
also
a
cue
to
detect
relativ
ely
g
r
adual
or
f
ast
change
in
the
scene
.
By
compar
ing
the
intensity
v
alue
of
pix
el
at
the
same
position
in
diff
erence
time
lapses
,
a
change
if
e
xist
should
be
detected.
One
w
a
y
to
do
is
to
compute
the
distance
betw
een
the
current
pix
el
v
alue
and
the
bac
kg
round
model
pix
el(s)
f
ollo
w
ed
b
y
a
compar
ison
with
a
threshold.
After
clas-
sification,
an
update
of
the
bac
kg
round
model
is
necessar
y
to
ensure
that
the
bac
kg
round
model
can
lear
n
the
changes
in
the
video
scene
or
to
lear
n
the
en
vironment
changes
.
In
this
paper
w
e
consider
that
the
estimation
of
the
distance
metr
ic
computed
to
tell
where
the
current
pix
el
v
alue
stands
should
include
the
w
a
y
that
the
bac
kg
round
model
is
updated.
In
this
paper
w
e
introduce
a
T
empor
al
based
approach
f
or
bac
kg
round
subtr
action
task,
using
a
sample
bac
kg
round
mod-
eling
with
adaptiv
e
threshold.
In
contr
ast
to
some
e
xisting
methods
which
consider
a
sampled
bac
kg
round
model,
the
considered
set
of
samples
is
directly
e
xploited
to
deter
mine
the
distance
metr
ics
[1,
2],
w
e
do
not
assume
that
the
bac
kg
round
samples
are
equally
distr
ib
uted,
in
f
act
the
proposed
approach
used
associated
w
eights
to
estimate
the
distance
betw
een
the
bac
kg
round
model
and
the
streaming
fr
ames
.
In
addition,
the
update
procedure
in
our
approach
neither
does
replace
the
sample
o
f
the
first
fr
ame
or
the
last
fr
ame
[1],
nor
choose
a
r
andom
location
to
update
[2].
The
proposed
method
applied
a
w
eight
re
lated
update
to
all
the
samples
in
the
bac
kg
round
model.
Receiv
ed
A
ugust
7,
2014;
Re
vised
September
14,
2014;
Accepted
October
2,
2014
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
ISSN:
2302-4046
7779
This
paper
is
organiz
ed
as
f
ollo
ws;
in
section
II,
a
shor
t
re
vie
w
of
bac
kg
round
subtr
action
(BS)
algor
ithms
dealing
with
diff
erent
video
sur
v
eillance
challenges
has
been
presented.
Section
III
in-
troduces
the
proposed
fr
ame
w
or
k
which
consists
of
(i)
a
tr
aining
phase
f
or
b
uilding
the
bac
kg
round
model,
(ii)
a
distance
metr
ic
estimation
and
a
v
ar
iab
le
threshold
ba
sed
decision
f
or
classifying
and
separ
ating
bac
kg
round
and
f
oreg
round
pix
els
,
and
(iii)
the
proposed
updating
str
ategy
adopted
in
the
fr
ame
w
or
k.
Exper
imental
results
of
the
proposed
scheme
and
a
discussion
of
the
issues
relat-
ed
to
noise
and
ghost
cancellation
are
presented
in
section
IV
.
The
fifth
section
is
de
v
oted
to
the
perf
or
mance
analysis
of
the
proposed
fr
ame
w
or
k
and
tab
les
a
compar
ison
betw
een
the
proposed
algor
ithm
and
some
e
xisting
bac
kg
round
subtr
action
approaches
.
2.
Related
w
ork
In
[1
],
the
authors
presented
a
non-par
ametr
ic
k
er
nel
density
estimation
(KDE)
f
or
bac
k-
g
round
and
f
oreg
round
mod
elling
using
a
shor
t
ter
m
and
a
long
ter
m
model
based
on
the
selection
of
N
bac
kg
round
model
samples
,
KDE
is
an
efficient
solution
to
mo
ving
object
e
xtr
action,
ho
w
e
v
er
it
needs
a
consider
ab
le
computational
cost.
The
authors
in
[3],
classified
e
xisting
bac
kg
round
sub-
tr
action
methods
into
recursiv
e
and
non-recursiv
e
approaches
,
and
made
a
compar
ison
betw
een
simple
basic
methods
and
probabilistic
modeling
based
approaches
,
their
e
xper
iment
s
sho
w
ed
that
e
v
en
basic
method
could
produce
good
results
,
while
the
computatio
nal
cost
k
ept
lo
w
.
A
f
ast
and
rob
ust
algor
ithm
f
or
bac
kg
round
subtr
action
w
as
proposed
in
[4],
the
authors
presented
a
ne
w
hier
archical
motion
detection
algor
ithm
based
on
sigma-delta
modulation.
The
y
ha
v
e
consid-
ered
a
conditional
approach
b
y
inser
ting
controllers
into
the
classification
and
the
update
process
.
In
[2],
a
po
w
erful
algor
ithm
f
or
bac
kg
round
modeling
and
f
oreg
round
e
xtr
action
named
the
Visual
Bac
kg
round
Extr
actor
(ViBe)
is
proposed;
the
algor
ithm
adopts
a
sample
based
bac
kg
round
mod-
eling
appro
ach
with
a
stochastic
replacement
and
a
spatial
diffusion
f
or
the
update
step
.
Using
a
constant
threshold
v
alue
f
or
bac
kg
round
/f
oreg
round
separ
ation;
ViBe
o
v
ercomes
most
of
bac
k-
g
round
subtr
action
challenges
.
It
has
been
argued
in
[5]
that
impro
v
ed
results
ha
v
e
been
f
ou
nd
b
y
using
a
threshold
as
the
half
of
the
standard
de
viation
computed
f
or
all
the
samples
in
the
bac
k-
g
round
model
o
v
er
time
.
The
authors
in
[
6]
used
to
w
threshold
v
alues
and
a
three
successiv
e
fr
ames
f
or
mo
ving
object
detection,
the
y
affir
med
that
such
a
consider
ation
is
strongly
adapted
to
the
en
vironment
changes
.
In
[7],
the
authors
introduced
an
algor
ithm
f
or
mo
ving
v
ehicle
detection
using
a
comb
ination
of
semantic
and
bac
kg
round
diff
erences
,
the
y
used
a
limited
threshold
v
alue
to
b
uild
the
binar
y
images
,
e
v
en
though
the
results
w
as
quite
impressiv
e
.
The
authors
in
[8]
dr
a
w
a
compar
ison
betw
een
a
set
of
bac
kg
round
subtr
action
techniques
using
v
ar
ious
distance
com-
putations
,
in
addition
the
y
ha
v
e
introduced
a
square
sum
of
diff
erences
betw
een
RGB
entr
ies
and
the
bac
kg
round
fr
ame
.
In
this
paper
,
w
e
propose
to
use
w
eighted
squared
diff
erences
betw
een
entr
y
fr
ames
and
the
bac
kg
round
model
as
a
distance
metr
ic
,
as
w
ell
as
e
xploiting
the
w
eights
on
the
update
procedure
.
The
f
ollo
wing
par
ag
r
aphs
present
the
e
xtents
of
the
proposed
approach,
and
detail
the
steps
and
the
choices
of
the
adopted
par
ameter
ization.
3.
PR
OPOSED
ALGORITHM:
AD
APTIVE
THRESHOLD
The
presented
algor
ithm
consists
of
three
phases:
tr
aining
and
bac
kg
round
modeling
stage
,
f
oreg
round/bac
kg
round
separ
ation
phase
,
and
an
update
step
.
In
the
f
ollo
wing;
w
e
detail
these
steps
and
justify
the
choices
that
w
ere
made
.
3.1.
Bac
kgr
ound
Modeling
A
non
par
ametr
ic
bac
kg
round
modeling
str
ategy
is
adopted
in
the
fr
ame
w
or
k,
the
bac
k-
g
round
model
is
considered
to
be
a
set
of
K
fr
ames
tak
en
dur
ing
the
initialization.
Let:
B
GM
(
x;
y
)
=
f
b
1
;
b
2
;
:
:
:
;
b
K
g
(1)
be
th
e
collection
of
K
bac
kg
round
samples
at
location
(
x;
y
)
,
where
b
m
;
m
=
1
;
2
;
:
:
:
;
K
are
the
samples
collected
at
diff
erent
times
at
location
(
x;
y
)
.
Moreo
v
er
,
a
w
eight
giv
en
b
y
equation
(2)
is
w
eighted
samples
f
or
bac
kg
round
subtr
action(Boubek
eur)
Evaluation Warning : The document was created with Spire.PDF for Python.
7780
ISSN:
2302-4046
associated
with
each
bac
kg
round
sample
,
W
i
=
1
=
2
i
1
P
K
m
=1
1
2
m
1
(2)
Where;
i
is
the
inde
x
of
the
sample
in
the
bac
kg
round
model,
the
v
alues
w
i
are
in
the
r
ange
[0
;
1]
,
the
nor
malization
of
the
w
eights
came
to
ensure
that
regardless
the
n
umber
of
samples
chosen
to
b
uild
the
BGM,
the
sum
of
the
w
eights
remains
equal
to
1.
If
no
f
oreg
round
object
is
present
dur
ing
the
initialization
step
,
the
set
of
K
pix
els
should
represent
a
r
ange
of
possib
le
v
alues
f
or
a
bac
kg
round
pix
el.
In
such
a
case
,
the
v
ar
iation
in
intensity
v
alue
o
v
er
time
f
or
B
GM
(
x;
y
)
should
not
be
significant
at
all,
w
e
introduce
then
in
equation
(3)
a
measure
S
(
x;
y
)
defined
as
the
mean
square
of
successiv
e
diff
erences
betw
een
bac
kg
round
model
samples
in
the
same
location
(
x;
y
)
.
S
(
x;
y
)
=
1
K
1
K
X
m
=2
(
b
m
b
m
1
)
2
(3)
W
e
are
interested
in
the
beha
vior
of
the
pix
el
belonging
to
the
bac
kg
round
model
at
location
(
x;
y
)
,
the
metr
ic
S
(
x;
y
)
tak
es
a
v
alues
according
to
the
deg
ree
of
similar
ity
betw
een
bac
kg
round
model
samples
.
3.2.
Foregr
ound/
Bac
kgr
ound
separation
In
the
algor
ithm,
a
decision
scheme
based
on
a
distance
met
r
ic
estimation
and
a
v
ar
iab
le
threshold
is
used
f
or
separ
ating
and
classifying
f
oreg
roun
d
and
bac
kg
roun
d
pix
els
.
The
proposed
f
oreg
round/
bac
kg
round
separ
ation
scheme
in
v
olv
es
tw
o
successiv
e
tests:
First
,
f
or
e
v
er
y
ne
w
pix
el;
a
w
eighted
distance
d
(
x;
y
)
metr
ic
is
estimated
using
equation
(4).
d
(
x;
y
)
=
1
K
K
X
m
=1
w
m
(
b
m
V
t
(
x;
y
))
(4)
Where
V
t
(
x;
y
)
denotes
a
current
pix
el
v
alue
at
location
(
x;
y
)
and
w
m
are
the
w
eights
v
alues
associated
with
bac
kg
round
model
samples
defined
in
equation
(2).
No
w
giv
en
the
v
alue
of
the
distance
computed
using
equation
(4),
this
calculated
v
alue
is
compared
to
the
metr
ic
estimated
b
y
equation
(3),
if
the
distance
is
g
reater
than
S
(
x;
y
)
;
the
pix
el
is
classified
a
pr
ior
i
as
a
f
ore-
g
round,
otherwise
is
considered
a
bac
kg
round
pix
el.
The
f
oreg
round
pix
els
are
labeled
1
and
the
bac
kg
round
pix
els
b
y
0.
Equation
(5)
sho
ws
the
pr
ior
obtained
binar
y
mask.
M
pr
ioir
(
x;
y
)
=
f
or
eg
r
ound
if
d
(
x;
y
)
>
S
(
x;
y
)
back
g
r
ound
if
d
(
x;
y
)
<
S
(
x;
y
)
(5)
Second,
b
y
analyzing
the
f
oreg
round
mask
p
ro
v
i
ded
b
y
this
first
test,
w
e
note
the
presence
of
ghost
pix
els
in
the
binar
y
mask,
especially
when
f
oreg
round
objects
are
present
in
the
scene
dur-
ing
the
initialization
phase
.
T
o
deal
with
this
challenge
and
in
order
to
mak
e
the
fr
ame
w
or
k
more
imm
une
to
the
prob
lem
of
ghost;
a
second
test
based
on
the
computation
of
the
diff
erence
be-
tw
een
successiv
e
fr
ames
and
making
use
of
the
metr
ic
S
(
x;
y
)
is
added
to
the
algor
ithm.
A
pr
ior
i
classified
f
oreg
round
pix
el
is
finally
v
alidated
as
a
f
oreg
round
pix
el
in
the
case
where
only
the
dis-
tance
betw
een
successiv
e
fr
ames
is
larger
than
the
measure
S
(
x;
y
)
,
otherwise
the
corresponding
pix
el
is
declared
as
a
bac
kg
round
pix
el.
Equation
(6)
sho
ws
the
obtained
labeled
mask.
M
(
x;
y
)
=
8
<
:
2
if
M
pr
ior
(
x;
y
)
=
1
and
D
if
f
(
x;
y
)
>
S
(
x;
y
)
1
if
M
pr
ior
(
x;
y
)
=
1
and
D
if
f
(
x;
y
)
<
S
(
x;
y
)
0
if
M
pr
ior
(
x;
y
)
=
0
(6)
and
D
if
f
(
x;
y
)
=
(
V
t
(
x;
y
)
V
t
1
(
x;
y
))
:
(
V
t
1
(
x;
y
)
V
0
(
x;
y
))
(7)
TELK
OMNIKA
V
ol.
12,
No
.
11,
No
v
ember
2014
:
7778
7784
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
ISSN:
2302-4046
7781
Only
pix
els
with
M
(
x;
y
)
=
2
are
actually
f
oreg
round
p
ix
e
ls
,
tw
o
types
of
bac
kg
round
pix
els
to
be
distinguished:
bac
kg
round
pix
els
with
M
(
x;
y
)
=
0
and
those
where
M
(
x;
y
)
=
1
.
In
the
equation
(7),
the
v
alue
V
0
(
x;
y
)
represents
the
first
fr
ame
,
in
f
act
in
our
fr
ame
w
or
k
w
e
do
not
mak
e
an
y
supposition
regarding
the
first
fr
ame
,
fur
ther
more
if
a
ghost
situation
occurs
,
it
w
ould
be
eliminated
b
y
the
diff
erence
betw
een
fr
ames
in
the
equation
(7).
3.3.
Update
phase
When
coming
to
upd
ating
the
bac
kg
round
model,
the
reader
can
distinguish
betw
een
tw
o
str
ategies:
the
b
lind
update
policy
and
the
conser
v
ativ
e
approach
str
ategy
.
In
the
b
lind
update
pro-
cedure
,
each
bac
kg
round
model
pix
el
is
updated
without
consider
ing
the
output
of
the
f
oreg
round/
bac
kg
round
separ
ation
phase
.
The
conser
v
ativ
e
approach
str
ategy
depends
on
the
result
of
the
classification
step
in
a
w
a
y
that
only
classified
bac
kg
round
pix
els
are
allo
w
ed
to
update
the
bac
k-
g
round
model
samples
,
as
a
consequence
bac
kg
round
samples
in
the
locations
corresponding
to
actually
classified
f
oreg
round
pix
els
remain
without
change
.
In
the
fr
ame
w
or
k,
the
proposed
updating
str
ategy
is
neither
a
b
lind
update
approach
nor
a
conser
v
ativ
e
one
.
A
conditional
w
eight-
ed
conser
v
ativ
e
update
str
ategy
is
presented
in
this
paper
;
ear
lier
;
w
e
ha
v
e
identified
tw
o
types
of
classified
bac
kg
round
pix
els
in
the
proposed
f
oreg
round/
bac
kg
round
separ
ation
stage:
pix
els
which
are
classified
bac
kg
round
f
rom
the
first
test,
and
other
pix
els
that
w
ere
first
declared
as
a
f
oreg
round
and
later
set
to
bac
kg
round
pix
els
using
t
he
second
test.
F
or
the
first
type
,
the
v
alue
of
those
pix
els
are
included
directly
in
the
bac
kg
round
model
samples
.
Moreo
v
er
f
or
t
he
second
type
and
f
or
tho
se
pix
els
which
their
corresponding
fr
ame
diff
erence
is
less
than
the
S
v
alue
,
bac
kg
round
model
updating
f
ollo
ws
a
w
eighted
v
alues
as
sho
wn
in
equation
(8)
B
GM
(
x;
y
)
new
=
w
m
:B
GM
(
x;
y
)
ol
d
+
(1
w
m
)
:V
(
x;
y
)
if
M
(
x;
y
)
=
1
V
(
x;
y
)
if
M
(
x;
y
)
=
0
(8)
More
e
xplicitly
,
at
each
location
and
f
or
the
first
type
of
classified
bac
kg
round
pix
els
where
M
(
x;
y
)
=
0
;
all
the
corresponding
bac
kg
round
samples
are
replaced
b
y
the
current
pix
el
v
alue
V
(
x;
y
)
.
F
or
the
second
type
of
bac
kg
round
pix
els
where
,
M
(
x;
y
)
=
1
,
the
updating
process
is
achie
v
ed
b
y
using
a
w
eighted
sum
betw
een
e
v
er
y
bac
kg
round
model
sample
and
the
current
pix
el
v
alue
,
this
update
can
be
understood
as
f
ollo
ws:
t
he
first
bac
kg
round
sample
k
eeps
w
1
of
its
o
wn
v
alue
and
gets
(1
w
1
)
from
the
current
pix
el
v
alue
.
The
ne
xt
bac
kg
round
pix
el
k
eeps
only
w
m
and
gets
the
(1
w
m
)
left
from
the
current
pix
el
v
alue
V
(
x;
y
)
.
The
increasing
percentage
of
the
current
fr
ame
v
alue
in
the
update
of
other
la
y
ers
e
xplains
that
fur
ther
la
y
er
updated
more
impact
obtained
b
y
the
bac
kg
round
model
samples
from
the
current
fr
ame
.
Fur
ther
more
,
the
metr
ic
defined
b
y
equation
(3)
should
be
updated
to
ensure
that
the
threshold
considered
f
or
fur
ther
decision
is
up
to
date
.
The
update
process
goes
as
mentioned
on
equation
(9).
S
(
x;
y
)
=
8
<
:
x
if
M
pr
ior
(
x;
y
)
=
1
1
K
1
P
K
m
=2
(
b
m
b
m
1
)
2
if
M
(
x;
y
)
=
1
S
(
x;
y
)
other
w
ise
(9)
Note
that
the
threshold
tak
es
the
v
alue
of
the
current
pix
el
v
alue
when
the
pix
el
is
classified
as
f
oreg
round
object.
This
v
alue
has
been
chosen
to
mak
e
sure
that
the
update
f
or
the
w
eighted
f
or
m
ula
presented
in
equation
(8)
be
eff
ectiv
e
f
or
pix
els
satisfying
M
(
x;
y
)
=
1
,
in
the
case
where
these
pix
els
are
classified
in
fur
ther
process
as
bac
kg
round
pix
els
.
4.
EXPERIMENT
AL
RESUL
TS
The
proposed
fr
ame
w
or
k
w
as
first
implemented
o
n
MA
TLAB
and
later
on
Visual
Stu-
dio
C++
to
test
its
real
time
perf
or
mance
.
Our
e
xper
iments
w
ere
conducted
on
an
I7
CPU
with
2.2
GHz,
the
Change
Detection
dataset
introduced
b
y
[
9]
and
pub
licly
a
v
ailab
le
on
www
.
changedetection.net
has
been
used.
This
data
set
contains
a
v
ar
iety
of
video
sequences
includ-
ing
most
of
the
challenges
that
usually
f
ace
bac
kg
round
subtr
action
algor
ithms
.
A
set
of
video
w
eighted
samples
f
or
bac
kg
round
subtr
action(Boubek
eur)
Evaluation Warning : The document was created with Spire.PDF for Python.
7782
ISSN:
2302-4046
sequences
ha
v
e
been
chosen
to
test
the
perf
or
mance
of
the
proposed
approach,
and
to
e
xper-
iment
our
algor
ithm
with
v
ar
ious
challenges
presented
in
this
dataset
in
its
2012
v
ersion.
The
n
umber
of
initials
fr
ames
chosen
to
initializ
e
the
bac
kg
round
model
is
considered
K
=
10
in
all
our
conducted
e
xper
iments
.
Figure
1
sho
ws
the
results
of
the
proposed
algor
ithm
using
fr
ames
from
the
baseline
categor
y
.Figure
2
illustr
ates
the
results
of
the
proposed
algor
ithm
applied
to
the
case
of
dynamic
bac
kg
round
presented
as
the
Canoe
sequence
.
In
order
to
challenge
our
algor
ithm
with
ghost
situation,
w
e
ha
v
e
considered
the
highw
a
y
sequence
with
the
860
th
fr
ame
as
the
initial
fr
ame
.
Figure
3
sho
ws
the
obtained
results
compared
to
the
adaptiv
e
mixture
of
Gaussian
intro-
duced
b
y
Zivk
o
vic
in
[10].
Exper
iments
sho
w
competitiv
e
results
compared
to
e
xisting
approaches
and
demonstr
ate
the
applicability
of
the
proposed
fr
ame
w
or
k
in
a
v
ar
iety
of
video
sur
v
eillance
s-
cenar
ios
.
In
the
f
ollo
wing,
w
e
will
discuss
the
perf
or
mance
of
the
proposed
scheme
,
and
mak
e
a
compar
ison
with
some
e
xisting
methods
and
algor
ithms
dealing
with
bac
kg
round
subtr
action.
Figure
1.
Proposed
method
f
or
Baseline
categor
y:
fr
ames
from
the
highw
a
y
sequence
.
5.
EV
ALU
A
TION
AND
DISCUSSION
In
most
of
the
liter
ature
,
the
perf
or
mance
of
Bac
kg
round/F
oreg
round
classification
is
es-
timated
b
y
consider
ing
a
ref
erence
as
g
round
tr
uth,
and
computing
se
v
er
al
metr
ics
.The
authors
in
[9]
presented
se
v
en
(07)
metr
ics
to
assess
the
efficiency
of
motion
segmentation
algor
ithms
,
through
the
computation
of
TP
,
TN,
FP
,
and
FN
that
are
respectiv
ely
tr
ue
positiv
e
count,
tr
ue
neg-
ativ
e
count,
f
alse
positiv
e
count,
and
the
f
alse
negativ
e
count.
The
se
v
en
metr
ic
presented
in
[9]
are
the
f
ollo
wing:
R
ecal
l
(
R
E
)
=
T
P
T
P
+
F
N
(10)
S
pecicity
(
S
P
)
=
T
N
T
N
+
F
P
(11)
F
al
s
e
P
ositiv
e
R
ate
(
F
P
R
)
=
F
P
F
P
+
T
N
(12)
F
al
se
N
eg
ativ
e
R
ate
(
F
N
R
)
=
F
N
T
N
+
F
P
(13)
P
er
centag
e
of
W
r
ong
C
l
assif
ication
(
P
W
C
)
=
100
:
F
N
+
F
P
T
P
+
F
N
+
F
P
+
T
N
(14)
P
r
ecision
(
P
R
)
=
T
P
T
P
+
F
P
(15)
F
measur
e
=
2
P
r
:R
e
P
r
+
R
e
(16)
In
order
to
e
v
aluate
the
proposed
scheme
,
w
e
ha
v
e
compared
the
obtained
results
with
those
pub
licly
a
v
ailab
le
on
the
change
detection
w
ebsite
(T
ab
le
2).
W
e
ha
v
e
computed
the
se
v
en
TELK
OMNIKA
V
ol.
12,
No
.
11,
No
v
ember
2014
:
7778
7784
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
ISSN:
2302-4046
7783
T
ab
le
1.
The
perf
or
mance
of
the
proposed
algor
ithm.
Sequence
RE
SP
FPR
FNR
PWC
PR
F-Measure
Highw
a
y
0,9340
0,9918
0,0082
0,0660
1,1581
0,8783
0,9053
Office
0,8056
0,9989
0,0011
0,1944
1,4431
0,9821
0,8852
P
edestr
ian
0,8966
0,9977
0,0023
0,1034
0,3251
0,7977
0,8443
PETS
2006
0,8368
0,9966
0,0034
0,1632
0,5509
0,7625
0,7979
Canoe
0.8014
0.9957
0.0043
0.1986
0.0114
0.8778
0.8379
Ov
er
pass
0.7188
0.9969
0.0031
0.2812
0.0069
0.7584
0.7381
metr
ics
sta
ted
ear
lier
.
The
proposed
fr
ame
w
or
k
achie
v
ed
a
percentage
of
missed
classification
less
than
1%
f
or
the
categor
y
of
baseline
,
ho
w
e
v
er
it
sho
ws
some
lac
k
of
precision
when
tested
with
sequences
including
dynamic
bac
kg
round.
By
analyzing
the
obtained
results
w
e
belie
v
e
that
the
proposed
fr
ame
w
or
k
is
v
er
y
suitab
le
f
or
video
based
tr
affic
monitor
ing
systems
.
The
proposed
method
has
sho
wn
rob
ustness
against
ghost
situation
due
to
the
conditional
instan-
taneous
update
of
the
bac
kg
round
model,
and
to
the
computation
of
the
distance
metr
ic
which
in
v
olv
ed
decreasing
w
eights
from
the
BGM.
The
tab
le
1,
sho
ws
the
results
obtained
when
applying
the
presented
algor
ithm
on
the
categor
y
of
baseline
with
the
high
sequences
.
The
proposed
algor
ithm
has
some
def
ects
when
consider
ing
the
other
categor
ies
(camer
a
jitters
,
Dynamic
bac
kg
round).
6.
CONCLUSION
AND
FUR
THER
W
ORK
In
this
paper
;
w
e
ha
v
e
presented
a
fr
ame
w
or
k
f
or
bac
kg
round
subtr
action
using
an
adap-
tiv
e
threshold
presented
as
the
mean
square
of
successiv
e
diff
erences
f
or
the
bac
kg
round
model
samples
,
and
a
w
eighted
distance
computation
f
or
estimating
the
distance
betw
een
incoming
fr
ames
and
the
bac
kg
round
model.
In
contr
ast
with
e
xisting
upd
ate
str
ategies
,
w
e
ha
v
e
intro-
duced
a
w
eighted
update
policy
to
the
bac
kg
round
model
based
on
associated
w
eights
to
ensure
the
accur
acy
in
the
distance
estimation
step
.
The
proposed
fr
ame
w
or
k
achie
v
ed
compar
ativ
e
re-
sults
when
consider
ing
sur
v
eillance
application
sequences
and
tr
affic
monitor
ing
systems
videos
.
The
presented
fr
ame
w
or
k
deals
with
prob
lem
of
ghost
and
noisy
scene
,
as
w
ell
as
the
g
r
adual
slo
w
illumination
change
.
In
fur
ther
w
or
k
w
e
seek
to
impro
v
e
the
presented
appr
oach
to
fit
an
e
xtensiv
e
r
ange
of
challenges:
dynamic
scene
and
bad
w
eather
situations
.
Figure
2.
Proposed
method
f
or
Dynamic
Bac
kg
round
Categor
y:
fr
ames
from
the
canoe
sequence
.
Ref
erences
[1]
Elgammal.
A.
et
al.,
“No
n-par
ametr
ic
model
f
or
bac
kg
round
subtr
action,
”
In
Computer
Vi-
sionECCV
2000
,
v
ol.
-,
pp
.
751–767,
2000.
w
eighted
samples
f
or
bac
kg
round
subtr
action(Boubek
eur)
Evaluation Warning : The document was created with Spire.PDF for Python.
7784
ISSN:
2302-4046
T
ab
le
2.
Compar
ison
b
etw
een
the
proposed
method
and
some
of
the
e
xisting
bac
kg
round
sub-
tr
action
algor
ithms
Method
RE
SP
FPR
FNR
PWC
PR
F-Measure
Proposed
0.8683
0.9963
0.0037
0.1317
0.8693
0.8582
0.8551
Mahalanobis
distance
[8]
0.3154
0.9991
0.0009
0.6846
2.8698
0.4642
0.9270
GMM/Zivk
o
vic
[10]
0.8085
0.9972
0.0028
0.1915
1.3298
0.8382
0.8993
Euclidean
distance
[8]
0.8385
0.9955
0.0045
0.1615
1.0260
0.8720
0.9114
Figure
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