Fuzzy Cellular Background Subtraction Technique for Urban Traffic Applications
World Applied Sciences Journal 5 (1): 000-000, 2008
ISSN 1818-4952
© IDOSI Publications, 2008
Fuzzy-Cellular Background Subtraction Technique for Urban Traffic Applications
Moein Shakeri, Hossein Deldari
Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract: Computational structure of cellular automata has attracted researchers and vastly been
used in various fields of science. Cellular automata perform complex computations with a high
degree of efficiency and robustness; they are especially suitable for modeling natural systems that
can be described as massive collections of simple objects interacting locally with each other, such
as motion detection in image processing. On the other hand, extraction of moving objects from an
image sequence is a fundamental problem in dynamic image analysis. A common method for realtime segmentation of moving regions in image sequences, involves “background subtraction.
Nowadays background modeling and subtraction algorithms are commonly used in real-time
urban traffic applications for detecting and tracking vehicles and monitoring streets. In this paper
by the use of cellular automata, a novel fuzzy approach for background subtraction with a
particular interest to the problem of vehicle detection is presented. Our experimental results
demonstrate that fuzzy-cellular system is much more efficient, robust and accurate than classical
approaches.
mo_sh88@stu-mail.um.ac.ir, hd@um.ac.ir
Key words: Fuzzy background subtraction • fuzzy-cellular background modeling • cellular
automata • vehicle detection
objects in subsequent processing. The complex traffic
flow at the intersection also poses challenges to a
background subtraction algorithm. The vehicles move
at a normal speed when the light is green, but come to a
stop when it turns red. The vehicles then remain
stationary until the light turns green again. A good
background subtraction algorithm must handle the
moving objects that first merge into the background and
then become foreground at a later time. In addition, to
accommodate the real-time needs of many applications,
a background subtraction algorithm must be
computationally inexpensive and have low memory
requirements, while still being able to accurately
identify moving objects in the videos [1].
According to importance of real-time computations
in the surveillance systems, improvement the efficiency
of simple background subtraction methods is so
significant. To this end, in this paper we propose a
novel fuzzy method based on computational model of
cellular automata for background subtraction and
moving object detection. Our experimental results
demonstrate that the fuzzy-cellular system is much
more efficient, robust and accurate than classical
approaches.
The reminder of the paper is organized as follows:
firstly cellular automata are introduced, and then we
briefly review background subtraction concepts.
INTRODUCTION
Identifying moving objects from a video sequence
is a fundamental and critical task in video surveillance,
traffic monitoring and analysis, human detection and
tracking, and gesture recognition in human-machine
interface. A common approach to identifying the
moving objects is background subtraction, where each
video frame is compared against a reference or
background model. Pixels in the current frame that
deviate significantly from the background are
considered to be moving objects. These “foreground"
pixels are further processed for object localization and
tracking. Since background subtraction is often the first
step in many computer vision applications, it is
important that the extracted foreground pixels
accurately correspond to the moving objects of interest.
There are several problems that a good background
subtraction algorithm must solve correctly. Consider a
video sequence from a stationary camera overlooking a
traffic intersection. As it is an outdoor environment, a
background subtraction algorithm should adapt to
various levels of illumination at different times of the
day and handle adverse weather condition such as fog
or snow that modifies the background. Changing
shadow, cast by moving objects, should be removed so
that consistent features can be extracted from the
Corresponding Author: Dr. Moein Shakeri, Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad,
Iran
1
ISSN 1818-4952
© IDOSI Publications, 2008
Fuzzy-Cellular Background Subtraction Technique for Urban Traffic Applications
Moein Shakeri, Hossein Deldari
Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract: Computational structure of cellular automata has attracted researchers and vastly been
used in various fields of science. Cellular automata perform complex computations with a high
degree of efficiency and robustness; they are especially suitable for modeling natural systems that
can be described as massive collections of simple objects interacting locally with each other, such
as motion detection in image processing. On the other hand, extraction of moving objects from an
image sequence is a fundamental problem in dynamic image analysis. A common method for realtime segmentation of moving regions in image sequences, involves “background subtraction.
Nowadays background modeling and subtraction algorithms are commonly used in real-time
urban traffic applications for detecting and tracking vehicles and monitoring streets. In this paper
by the use of cellular automata, a novel fuzzy approach for background subtraction with a
particular interest to the problem of vehicle detection is presented. Our experimental results
demonstrate that fuzzy-cellular system is much more efficient, robust and accurate than classical
approaches.
mo_sh88@stu-mail.um.ac.ir, hd@um.ac.ir
Key words: Fuzzy background subtraction • fuzzy-cellular background modeling • cellular
automata • vehicle detection
objects in subsequent processing. The complex traffic
flow at the intersection also poses challenges to a
background subtraction algorithm. The vehicles move
at a normal speed when the light is green, but come to a
stop when it turns red. The vehicles then remain
stationary until the light turns green again. A good
background subtraction algorithm must handle the
moving objects that first merge into the background and
then become foreground at a later time. In addition, to
accommodate the real-time needs of many applications,
a background subtraction algorithm must be
computationally inexpensive and have low memory
requirements, while still being able to accurately
identify moving objects in the videos [1].
According to importance of real-time computations
in the surveillance systems, improvement the efficiency
of simple background subtraction methods is so
significant. To this end, in this paper we propose a
novel fuzzy method based on computational model of
cellular automata for background subtraction and
moving object detection. Our experimental results
demonstrate that the fuzzy-cellular system is much
more efficient, robust and accurate than classical
approaches.
The reminder of the paper is organized as follows:
firstly cellular automata are introduced, and then we
briefly review background subtraction concepts.
INTRODUCTION
Identifying moving objects from a video sequence
is a fundamental and critical task in video surveillance,
traffic monitoring and analysis, human detection and
tracking, and gesture recognition in human-machine
interface. A common approach to identifying the
moving objects is background subtraction, where each
video frame is compared against a reference or
background model. Pixels in the current frame that
deviate significantly from the background are
considered to be moving objects. These “foreground"
pixels are further processed for object localization and
tracking. Since background subtraction is often the first
step in many computer vision applications, it is
important that the extracted foreground pixels
accurately correspond to the moving objects of interest.
There are several problems that a good background
subtraction algorithm must solve correctly. Consider a
video sequence from a stationary camera overlooking a
traffic intersection. As it is an outdoor environment, a
background subtraction algorithm should adapt to
various levels of illumination at different times of the
day and handle adverse weather condition such as fog
or snow that modifies the background. Changing
shadow, cast by moving objects, should be removed so
that consistent features can be extracted from the
Corresponding Author: Dr. Moein Shakeri, Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad,
Iran
1