Introduction

Background subtraction is a widely used technique to detect moving objects or abandoned objects in video sequences. The last decade witnessed very significant contributions for various applications, as in video-surveillance, optical motion capture and multimedia applications.

A background subtraction algorithm consists in the following steps: background modeling, background initialization, background maintenance and foreground detection. The simplest way to model the background is to acquire a background image which does not include any moving object. In many environments, the empty background is not available and can always be changed under critical situations, like illumination changes and dynamic backgrounds. To take into account these problems of robustness and adaptation, many background modeling methods have been developed over this last decade and can be grouped in the following categories:

• Basic background modelling, such as Average, Median, Histogram analysis over time
  
• Statistical background modeling, such as Single Gaussian, Mixture of Gaussians, Kernel density estimation, Subspace learning
  
• Fuzzy background modeling, such as Fuzzy running average, Type-2 fuzzy mixture of Gaussians
  
• Background clustering, such as K-means, Codebook
  
• Background estimation, such as Wiener filter, Kalman filter, Tchebychev filter

The goals of this special issue are twofold: 1) grouping recent advances in background subtraction methods in different applications and 2) developing new methods to perform background subtraction algorithms taking into account critical background maintenance situations.

 

Scope/Topics of Interest

Manuscripts are solicited to address a wide range of mathematical tools to perform the background subtraction steps, including but not limited to the following:

• Extensions of well-known statistical models
• Supervised, semi-supervised and unsupervised subspace learning
• Probabilistic graphical models
• Support vector machine, support vector regression, support vector data description
• Fuzzy concepts: theory of fuzzy sets, fuzzy logics
• Extensions of clustering tools
• Extensions of filters

Important Dates

Submission of papers due: February 02, 2010
Notification of revision: March 31, 2010
Final revised paper due: April 30, 2010
Acceptance Notification: May 31, 2010
Tentative publication date: July 31, 2010

Submission Guidelines

Manuscripts (6-10 pages in the MASAUM Journal of Basic and Applied Sciences publishing format) should be submitted electronically in .doc and .pdf formats by email to background.subtraction@gmail.com.

Please prepare your manuscript by following carefully the Instruction for Authors at http://www.masaumnet.com/instructionsforauthors.html.

Guest Editors

Thierry BOUWMANS, Université de La Rochelle, France.
Lucia MADDALENA, National Research Council, Italy.
Fida EL BAF, Université de La Rochelle, France.
Jordi GONZALEZ , Universitat Autonoma de Barcelona, Spain.
Alfredo PETROSINO, University of Naples "Parthenope", Italy.
Shengping ZHANG, Harbin Institute of Technology, China.

Contact Us

Thierry BOUWMANS
Laboratoire MIA
Université de La Rochelle
17000 La Rochelle
France
Tel : +(33)05.46.45.72.02
Mel : tbouwman@univ-lr.fr

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