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Study of Multi-target Tracking Algorithm Based on Mean-shift and Particle Filter

In: Liss 2014

Author

Listed:
  • Lijing Huang

    (Hebei University of Science and Technology)

  • Naiwen Yu

    (Hebei University of Science and Technology)

  • Ming Han

    (Yanshan University)

  • Peng Liu

    (Hebei University of Science and Technology)

Abstract

Target tracking in video sequences is an important part of information management. To combine the advantages of mean-shift tracking algorithm’s real-time and particle filter tracking algorithm’s robustness, this paper proposes a kind of particle filter multi-target tracking algorithm that based on weighted mean-shift. Firstly, we introduce non-parametric fast pattern matching algorithm of Kernel density estimation into particle filter, and iteratively calculate the probability density estimation. Then, the particle gradient direction is estimated by mean shift, and the mean for each particle moved to the sample is calculated. When the position of particle is changed, the weighted processing of the resampling particles will be done. Finally, sampling the new particles based on our algorithm. That can solve the particle degradation phenomena effectively and improve the status estimation accuracy. The algorithm that we proposed has been applied to multi-target tracking, and the experiments have shown the feasibility and effectiveness of the algorithm.

Suggested Citation

  • Lijing Huang & Naiwen Yu & Ming Han & Peng Liu, 2015. "Study of Multi-target Tracking Algorithm Based on Mean-shift and Particle Filter," Springer Books, in: Zhenji Zhang & Zuojun Max Shen & Juliang Zhang & Runtong Zhang (ed.), Liss 2014, edition 127, pages 1717-1724, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-43871-8_247
    DOI: 10.1007/978-3-662-43871-8_247
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