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A Sampling Algorithm for Tracking Multiple Objects

  • Hai Tao
  • Harpreet S. Sawhney
  • Rakesh Kumar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1883)

Abstract

The recently proposed CONDENSATION algorithm and its variants enable the estimation of arbitrary multi-modal posterior distributions that potentially represent multiple tracked objects. However, the specific state representation adopted in the earlier work does not explicitly supports counting, addition, deletion and occlusion of objects. Furthermore, the representation may increasingly bias the posterior density estimates towards objects with dominant likelihood as the estimation progresses over many frames. In this paper, a novel formulation and an associated CONDENSATION-like sampling algorithm that explicitly support counting, addition and deletion of objects are proposed. We represent all objects in an image as an object configuration. The a posteriori distribution of all possible configurations are explored and maintained using sampling techniques. The dynamics of configurations allow addition and deletion of objects and handle occlusion. An efficient hierarchical algorithm is also proposed to approximate the sampling process in high dimensional space. Promising comparative results on both synthetic and real data are demonstrated.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Hai Tao
    • 1
  • Harpreet S. Sawhney
    • 1
  • Rakesh Kumar
    • 1
  1. 1.Sarnoff CorporationPrinceton

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