Background Subtraction Framework Based on Local Spatial Distributions

  • Pierre-Marc Jodoin
  • Max Mignotte
  • Janusz Konrad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)

Abstract

Most statistical background subtraction techniques are based on the analysis of temporal color/intensity distributions. However, learning statistics on a series of time frames can be problematic, especially when no frames absent of moving objects are available or when the available memory isn’t sufficient to store the series of frames needed for learning. In this paper, we propose a framework that allows common statistical motion detection methods to use spatial statistics gathered on one frame instead of a series of frames as is usually the case. This simple and flexible framework is suitable for various applications including the ones with a mobile background such as when a tree is shaken by wind or when the camera jitters. Three statistical background subtraction methods have been adapted to the proposed framework and tested on different synthetic and real image sequences.

Keywords

Background subtraction spatial distributions 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pierre-Marc Jodoin
    • 1
  • Max Mignotte
    • 1
  • Janusz Konrad
    • 2
  1. 1.D.I.R.O. Université de MontréalMontréal, QcCanada
  2. 2.Department of Electrical and Computer Engineering Boston UniversityBostonUSA

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