TY - CONF T1 - Efficient particle filter-based tracking of multiple interacting targets using an MRF-based motion model T2 - 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings Y1 - 2003 A1 - Zia Khan A1 - Balch, T. A1 - Dellaert, F. KW - collision avoidance KW - computational cost KW - Computational efficiency KW - Educational institutions KW - exponential complexity KW - Filtering KW - filtering theory KW - Insects KW - joint particle tracker KW - Markov processes KW - Markov random field motion KW - Markov random fields KW - multiple interacting targets KW - particle filter-based tracking KW - Particle filters KW - Particle tracking KW - Radar tracking KW - social insect tracking application KW - target tracking KW - Trajectory AB - We describe a multiple hypothesis particle filter for tracking targets that are influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost. JA - 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings VL - 1 ER -