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Probabilistic Image-Based Tracking: Improving Particle Filtering

  • Daniel Rowe
  • Ignasi Rius
  • Jordi Gonzàlez
  • Xavier Roca
  • Juan J. Villanueva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3522)

Abstract

Condensation is a widely-used tracking algorithm based on particle filters. Although some results have been achieved, it has several unpleasant behaviours. In this paper, we highlight these misbehaviours and propose two improvements. A new weight assignment, which avoids sample impoverishment, is presented. Subsequently, the prediction process is enhanced. The proposal has been successfully tested using synthetic data, which reproduces some of the main difficulties a tracker must deal with.

Keywords

Particle Filter Visual Tracking Background Clutter Weight Assignment Recursive Estimation 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Daniel Rowe
    • 1
  • Ignasi Rius
    • 1
  • Jordi Gonzàlez
    • 1
  • Xavier Roca
    • 1
  • Juan J. Villanueva
    • 1
  1. 1.Computer Vision Centre/Department of Computing ScienceUniversitat Autònoma de BarcelonaBellaterra, BarcelonaSpain

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