TY - Generic T1 - MCMC-based particle filtering for tracking a variable number of interacting targets. Y1 - 2005 A1 - Khan, Zia A1 - Balch, Tucker A1 - Dellaert, Frank KW - algorithms KW - Animals KW - Artificial Intelligence KW - Computer simulation KW - HUMANS KW - Image Enhancement KW - Image Interpretation, Computer-Assisted KW - Information Storage and Retrieval KW - Markov chains KW - Models, Biological KW - Models, Statistical KW - Monte Carlo Method KW - Motion KW - Movement KW - Pattern Recognition, Automated KW - Subtraction Technique KW - Video Recording AB -

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.

JA - IEEE Trans Pattern Anal Mach Intell VL - 27 CP - 11 M3 - 10.1109/TPAMI.2005.223 ER -