2004
IEEE
International Conference on Systems, Man and Cybernetics
Background subtraction techniques: a review*
Massimo
Piccardi
Computer Vision
Group,
Faculty
of
Information Technology
University
of
Technology, Sydney
(UTS),
Australia
masi;imo@it.uts.edu.au
Abstract
-
Background subtraction is
a
widely used
approoch for detecting moving objects @om static
cameras. Mony different methods have been proposed
over the recent years and both the novice and the exprt
can be confused about iheir benefits and limitations. In
order
to
overcome this problem, this poper provides
a
review
of
ihe main methods and an original
categorisotion based on speed, memoy requirements and
accuracy, Such
o
review can effectively guide ihe
designer
to
select the most suitoble meihod for
a
given
application in
a
principled
way.
Methods reviewed
include parametric and non-porametric background
density estimates and spatial correlation approaches.
Keywords: background subtraction, moving object
detection, parametric and non-parametric approach’zs,
spatial correlation.
1
Introduction
Background subtraction is a widely used approach
for detecting moving objects in videos from static
cameras. The rationale in the approach is that of detecting
the moving objects from the difference between the
current frame and a reference frame, often called The
“background image”,
or
“background model”.
As
a baric,
the background image must be a representation of the
scene with no moving objects and must be kept regularly
updated
so
as to adapt to the varying luminarice
conditions and geometry settings. More complex models
have extended the concept of “background subtracticn”
beyond its literal meaning.
Several methods for performing background
subtraction have been proposed in the recent literamre.
All of these methods try to effectively estimate ihe
background model from the temporal sequence of the
frames. However, there is a wide variety of techniques
and both the expert and the newcomer to this area can be
confused about the benefits and limitations of each
method. This paper provides a thorough review of Ihe
main methods (with inevitable exclusions due to spwe
restrictions) and an original categorisation based on
speed, memory requirements and accuracy.
The rest of the paper is organized as follows: Section
2
describes the main features of each method reviewed.
Section
3
presents the comparison of speed, memory
requirements and accuracy, in this order. Conclusive
remarks are addressed at the end of this paper.
2
The reviewed approaches:
from simple to complex
The approaches reviewed
in
this paper range from
simple approaches, aiming to maximise speed and limiting
the memory requirements, to more sophisticated
approaches, aiming to achieve the highest possible
accuracy under any possible circumstances. All
approaches aim, however, at real-time performance, hence
a lower bound on speed always exists. The methods
reviewed
in
the following are:
’
Running Gaussian average
.
Temporal median filter
.
Mixture of Gaussians
f
Kernel density estimation
(KDE)
.
Sequential
KD
approximation
.
Cooccurence of image variations
.
Eigenbackgrounds
2.1
Running
Gaussian average
Wren
et
01.
in
[l]
have proposed to model the
background independently at each
(iJ)
pixel location. The
model is based on ideally fitting a Gaussian probability
density function @do on the last
n
pixel’s values. In order
to avoid fitting the pdf from scratch at each new frame
time,
t,
a running
(or
on-line cumulative) average is
computed instead as:
,u,
=
4
+
(1
-
a)P,.,
(1))
where
I,
is the pixel’s current value and
,u,
the previous
average;
a
is an empirical weight often chosen as a trade-
off between stability and quick update. Although not
stated explicitly in
[l],
the other parameter of the
Gaussian pdf, the standard deviation
cr,,
can be computed
similarly. In addition
to
speed,
the advantage of the
*
0-7803-8566-7/04/$20.00
0
2004
IEEE.
3099
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