MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
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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 multi-target 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 multi-target 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.
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Date
2005
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