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Statistical Unbiased Background Modeling for Moving Platforms

  • Michael Kirchhof
  • Uwe Stilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6952)

Abstract

Statistical background modeling is a standard technique for the detection of moving objects in a static scene. Nevertheless, the stateof-the-art approaches have several lacks for short sequences or quasistationary scenes. Quasi-static means that the ego-motion of the sensor is compensated by image processing. Our focus of attention goes back to the modeling of the pixel process, as it was introduced by Stauffer and Grimson. For quasi-stationary scenes the assignment of a pixel to an origin is uncertain. This assignment is an independent random process that contributes to the gray value. Since the typical update schemes are biased we introduce a novel update scheme based on the join mean and join variance of two independent distributions. The presented method can be seen as an update for the initial guess for more sophisticated algorithms that optimize the spatial distribution.

Keywords

Unbiased Estimator Background Modeling Registration Error Foreground Object Individual 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 2011

Authors and Affiliations

  • Michael Kirchhof
  • Uwe Stilla
    • 1
  1. 1.Photogrammetry and Remote SensingTechnische Universitaet MuenchenGermany

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