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Improved Background Mixture Models for Video Surveillance Applications

  • Chris Poppe
  • Gaëtan Martens
  • Peter Lambert
  • Rik Van de Walle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

Background subtraction is a method commonly used to segment objects of interest in image sequences. By comparing new frames to a background model, regions of interest can be found. To cope with highly dynamic and complex environments, a mixture of several models has been proposed. This paper proposes an update of the popular Mixture of Gaussian Models technique. Experimental analysis shows a lack of this technique to cope with quick illumination changes. A different matching mechanism is proposed to improve the general robustness and a comparison with related work is given. Finally, experimental results are presented to show the gain of the updated technique, according to the standard scheme and the related techniques.

Keywords

Gaussian Mixture Model Background Model Foreground Object Foreground Pixel Current Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Chris Poppe
    • 1
  • Gaëtan Martens
    • 1
  • Peter Lambert
    • 1
  • Rik Van de Walle
    • 1
  1. 1.Ghent University - IBBT, Department of Electronics and Information Systems - Multimedia Lab, Gaston Crommenlaan 8, B-9050 Ledeberg-GhentBelgium

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