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Making Background Subtraction Robust to Sudden Illumination Changes

  • Julien Pilet
  • Christoph Strecha
  • Pascal Fua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)

Abstract

Modern background subtraction techniques can handle gradual illumination changes but can easily be confused by rapid ones. We propose a technique that overcomes this limitation by relying on a statistical model, not of the pixel intensities, but of the illumination effects. Because they tend to affect whole areas of the image as opposed to individual pixels, low-dimensional models are appropriate for this purpose and make our method extremely robust to illumination changes, whether slow or fast.

We will demonstrate its performance by comparing it to two representative implementations of state-of-the-art methods, and by showing its effectiveness for occlusion handling in a real-time Augmented Reality context.

Keywords

Input Image Augmented Reality Gaussian Mixture Model Background Subtraction Background Model 
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 2008

Authors and Affiliations

  • Julien Pilet
    • 1
  • Christoph Strecha
    • 1
  • Pascal Fua
    • 1
  1. 1.École Polytechnique Fédérale de LausanneSwitzerland

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