An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets

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 exponen...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Zia Khan, Tucker R. Balch, Frank Dellaert
• Keywords: Chain Monte Carlo, Computer Vision, ECCV 2004, Joint Particle Filter, Markov Chain Monte, MCMC Sampling Step, Particle Filter Suffers