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Guided Importance Sampling Based Particle Filtering for Visual Tracking

  • Kazuhiko Kawamoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)

Abstract

Linear estimation based sequential importance sampling methods for particle filters are proposed that can be used to model the rapid change of object motion in a video sequence. First a linear least–squares estimation is used to build a proposal function from observations, and then it is extended to a robust linear estimation. These sampling methods give a framework for tracking objects whose motion cannot be well modeled by a prior model. Finally a switching algorithm between the proposed method and the prior model based sampling method is proposed to achieve a filtering of both smooth and rapid evolution of the state. The ability of the proposed method is illustrated on a real video sequence involving a rapidly moving object.

Keywords

Prior Distribution Feature Point Importance Sampling Prior Model Visual Tracking 
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

  • Kazuhiko Kawamoto
    • 1
  1. 1.Kyushu Institute of TechnologyKitakyushuJapan

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