@conference {19659,
	title = {Efficient particle filter-based tracking of multiple interacting targets using an MRF-based motion model},
	booktitle = {2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings},
	volume = {1},
	year = {2003},
	month = {2003/10//},
	pages = {254 - 259 vol.1},
	abstract = {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.},
	keywords = {collision avoidance, computational cost, Computational efficiency, Educational institutions, exponential complexity, Filtering, filtering theory, Insects, joint particle tracker, Markov processes, Markov random field motion, Markov random fields, multiple interacting targets, particle filter-based tracking, Particle filters, Particle tracking, Radar tracking, social insect tracking application, target tracking, Trajectory},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
