Original Paper

Machine Vision and Applications

, Volume 20, Issue 6, pp 395-409

First online:

Non-parametric statistical background modeling for efficient foreground region detection

  • Alireza TavakkoliAffiliated withComputer Vision Lab, University of Nevada Email author 
  • , Mircea NicolescuAffiliated withComputer Vision Lab, University of Nevada
  • , George BebisAffiliated withComputer Vision Lab, University of Nevada
  • , Monica NicolescuAffiliated withRobotics Laboratory, University of Nevada

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Abstract

Most methods for foreground region detection in videos are challenged by the presence of quasi-stationary backgrounds—flickering monitors, waving tree branches, moving water surfaces or rain. Additional difficulties are caused by camera shake or by the presence of moving objects in every image. The contribution of this paper is to propose a scene-independent and non-parametric modeling technique which covers most of the above scenarios. First, an adaptive statistical method, called adaptive kernel density estimation (AKDE), is proposed as a base-line system that addresses the scene dependence issue. After investigating its performance we introduce a novel general statistical technique, called recursive modeling (RM). The RM overcomes the weaknesses of the AKDE in modeling slow changes in the background. The performance of the RM is evaluated asymptotically and compared with the base-line system (AKDE). A wide range of quantitative and qualitative experiments is performed to compare the proposed RM with the base-line system and existing algorithms. Finally, a comparison of various background modeling systems is presented as well as a discussion on the suitability of each technique for different scenarios.

Keywords

Non-parametric density estimation Recursive modeling Background subtraction Background modeling Quasi-stationary backgrounds