An MCMC-based Particle Filter For Tracking Multiple Interacting Targets

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An MCMC-based Particle Filter For Tracking Multiple Interacting Targets (2003)

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by Zia Khan , Tucker Balch , Frank Dellaert

Venue:	in Proc. ECCV
Citations:	152 - 6 self

BibTeX

@INPROCEEDINGS{Khan03anmcmc-based,
    author = {Zia Khan and Tucker Balch and Frank Dellaert},
    title = {An MCMC-based Particle Filter For Tracking Multiple Interacting Targets},
    booktitle = {in Proc. ECCV},
    year = {2003},
    pages = {279--290}
}

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Abstract

We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the identity of targets throughout an interaction, drastically reducing tracker failures. The paper presents two main contributions: (1) we show how a Markov random field (MRF) motion prior, built on the fly at each time step, can substantially improve tracking when targets interact, and (2) we show how this can be done efficiently using Markov chain Monte Carlo (MCMC) sampling. We prove that incorporating an MRF to model interactions is equivalent to adding an additional interaction factor to the importance weights in a joint particle filter. Since a joint particle filter suffers from exponential complexity in the number of tracked targets, we replace the traditional importance sampling step in the particle filter with an MCMC sampling step. The resulting filter deals efficiently and effectively with complicated interactions when targets approach each other. We present both qualitative and quantitative results to substantiate the claims made in the paper, including a large scale experiment on a video-sequence of over 10,000 frames in length.

Keyphrases

multiple interacting target mcmc-based particle filter markov chain monte carlo particle filter markov random field exponential complexity additional interaction factor joint particle filter suffers filter deal traditional importance main contribution time step importance weight joint particle filter tracked target sophisticated motion model large scale experiment joint tracker quantitative result data association problem traditional approach tracker failure

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