TY - CHAP
T1 - An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets
T2 - Computer Vision - ECCV 2004
Y1 - 2004
A1 - Zia Khan
A1 - Balch, Tucker
A1 - Dellaert, Frank
ED - Pajdla, Tomás
ED - Matas, Jiří
KW - Artificial Intelligence (incl. Robotics)
KW - Computer Graphics
KW - Image Processing and Computer Vision
KW - pattern recognition
AB - 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 exponential complexity in the number of tracked targets, we replace the traditional importance sampling step in the particle filter with an MCMC sampling step. The resulting filter deals efficiently and effectively with complicated interactions when targets approach each other. We present both qualitative and quantitative results to substantiate the claims made in the paper, including a large scale experiment on a video-sequence of over 10,000 frames in length.
JA - Computer Vision - ECCV 2004
T3 - Lecture Notes in Computer Science
PB - Springer Berlin Heidelberg
SN - 978-3-540-21981-1, 978-3-540-24673-2
UR - http://link.springer.com/chapter/10.1007/978-3-540-24673-2_23
ER -