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Visibility-Based Observation Model for 3D Tracking with Non-parametric 3D Particle Filters

  • Raúl Mohedano
  • Narciso García
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5876)

Abstract

This paper presents a novel and powerful Bayesian framework for 3D tracking of multiple arbitrarily shaped objects, allowing the probabilistic combination of the cues captured from several calibrated cameras directly into the 3D world without assuming ground plane movement. This framework is based on a new interpretation of the Particle Filter, in which each particle represent the situation of a particular 3D position and thus particles aim to represent the volumetric occupancy pdf of an object of interest. The particularities of the proposed Particle Filter approach have also been addressed, resulting in the creation of a multi-camera observation model taking into account the visibility of the individual particles from each camera view, and a Bayesian classifier for improving the multi-hypothesis behavior of the proposed approach.

Keywords

Particle Filter Ground Plane Observation Model Camera View Conditional Independence Assumption 
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 2009

Authors and Affiliations

  • Raúl Mohedano
    • 1
  • Narciso García
    • 1
  1. 1.Grupo de Tratamiento de ImágenesUniversidad Politécnica de MadridMadridSpain

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