Probabilistic Model-Based Background Subtraction

  • V. Krüger
  • J. Anderson
  • T. Prehn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

In this paper we introduce a model-based background subtraction approach where first silhouettes, which model the correlations between neightboring pixels are being learned and where then Bayesian propagation over time is used to select the proper silhouette model and tracking parameters. Bayes propagation is attractive in our application as it allows to deal with uncertainties in the video data during tracking. We eploy a particle filter for density estimation. We have extensively tested our approach on suitable outdoor video data.

Keywords

Foreground Object Foreground Pixel Feature Extraction Technique Gait Recognition Tracking Parameter 
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 2005

Authors and Affiliations

  • V. Krüger
    • 1
  • J. Anderson
    • 2
  • T. Prehn
    • 2
  1. 1.Aalborg Media LabAalborg University, CopenhagenBallerup
  2. 2.Aalborg University EsbjergEsbjergDenmark

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