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Distributed Markov Chain Monte Carlo kernel based particle filtering for object tracking

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Abstract

Particle filters are computationally intensive and thus efficient parallelism is crucial to effective implementations, especially object tracking in video sequences. Two schemes for pipelining particles under high performance computing environment, including an alternative Markov Chain Monte Carlo (MCMC) resampling algorithm and kernel function, are proposed so as to improve tracking performance and minimize execution time. Experimental results on a network of workstations composed of simple off-the-shelf hardware components show that global parallelizable scheme provides a promising resolution to clearly reduce execution time with increasing particles, compared with generic particle filtering.

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Acknowledgement

This research was supported by the National Science Foundation of China under Grants 60572041 and 60832004.

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Authors and Affiliations

  1. School of Information and Engineering, Communication University of China, Beijing, China

    Danling Wang & Qin Zhang

  2. Department of Computer Science, The University of Auckland, Auckland, New Zealand

    John Morris

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  1. Danling Wang
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  2. Qin Zhang
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  3. John Morris
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Correspondence to Danling Wang.

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Wang, D., Zhang, Q. & Morris, J. Distributed Markov Chain Monte Carlo kernel based particle filtering for object tracking. Multimed Tools Appl 56, 303–314 (2012). https://doi.org/10.1007/s11042-010-0646-4

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  • DOI: https://doi.org/10.1007/s11042-010-0646-4

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