AbsKhanECCV < LCI

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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.
-- Main.simra - 26 Sep 2005
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Topic revision: r1 - 2005-09-26 - RobertSim