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Issue No.11 - November (2005 vol.27)
pp: 1805-1918
ABSTRACT
We describe a particle filter that effectively deals with interacting targets—targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.
INDEX TERMS
Index Terms- Particle filters, multitarget tracking, Markov random fields, Markov chain Monte Carlo.
CITATION
Zia Khan, Tucker Balch, Frank Dellaert, "MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.27, no. 11, pp. 1805-1918, November 2005, doi:10.1109/TPAMI.2005.223
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