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Video tracking based on sequential particle filtering on graphs.

Pan Pan1, Dan Schonfeld

  • 1Information Technology Laboratory, Fujitsu R&D Center Co., Ltd., Beijing 100025, China. ppan@cn.fujitsu.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 2, 2010
PubMed
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This study introduces a new particle filtering method for general graphs, offering an exact solution for directed acyclic graphs and an approximate solution for graphs with cycles, improving video tracking performance.

Area of Science:

  • Computer Vision
  • Graph Theory
  • Machine Learning

Background:

  • Particle filtering is crucial for state estimation in dynamic systems.
  • Applying particle filtering to general graphs presents significant computational challenges.
  • Existing methods struggle with complex graph structures, particularly those with cycles.

Purpose of the Study:

  • To develop a novel and efficient particle filtering solution for general graphs.
  • To provide an exact method for directed acyclic graphs and an approximate method for general graphs.
  • To enhance performance in video tracking applications through improved graph-based particle filtering.

Main Methods:

  • Developed an exact particle filtering solution for directed acyclic graphs using antichain decomposition and high-order Markov chains.

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  • Derived a closed-form sequential updating scheme for conditional density propagation.
  • Proposed an approximate solution for general graphs by decomposing them into directed acyclic subgraphs.

    • Main Results:

    • The proposed method achieves exact particle filtering on directed acyclic graphs.
    • An approximate yet effective particle filtering approach was established for general graphs with cycles.
    • Demonstrated superior performance in object tracking and distributed multi-object tracking video applications.

    Conclusions:

    • The novel particle filtering approach effectively handles general graph structures.
    • The method offers significant improvements over existing techniques in video tracking tasks.
    • This work advances the application of particle filtering in complex graphical models.