Background Segmentation Beyond RGB

  • Fredrik Kristensen
  • Peter Nilsson
  • Viktor Öwall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3852)


To efficiently classify and track video objects in a surveillance application, it is essential to reduce the amount of streaming data. One solution is to segment the video into background, i.e. stationary objects, and foreground, i.e. moving objects, and then discard the background. One such motion segmentation algorithm that has proven reliable is the Stauffer and Grimson algorithm. This paper investigates how different color spaces affect the segmentation result in terms of noise and shadow sensitivity. Shadows are especially problematic since they not only distort shape but can also result in falsely connected objects that will complicate tracking and classification. Therefore, a new decision kernel for the segmentation algorithm is presented. This kernel alters the probability of foreground detection to reduce shadows and to increase the chance of correct segmentation for objects with a skin tone color, e.g. faces.


Color Space Segmentation Algorithm Color Channel Skin Tone Shadow Detection 
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 2006

Authors and Affiliations

  • Fredrik Kristensen
    • 1
  • Peter Nilsson
    • 1
  • Viktor Öwall
    • 1
  1. 1.CCCD, Dept. of ElectroscienceLund UniversityLundSweden

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