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Boosted Markov Chain Monte Carlo Data Association for Multiple Target Detection and Tracking

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4 Author(s)
Qian Yu ; IRIS, Southern California Univ., Southern California University, CA ; Cohen, I. ; Medioni, G. ; Bo Wu

In this paper, we present a probabilistic framework for automatic detection and tracking of objects. We address the data association problem by formulating the visual tracking as finding the best partition of a measurement graph containing all detected moving regions. In order to incorporate model information in tracking procedure, the posterior distribution is augmented with Adaboost image likelihood. We adopt a MRF-based interaction to model the inter-track exclusion. To avoid the exponential complexity, we apply Markov chain Monte Carlo (MCMC) method to sample the solution space efficiently. We take data-oriented sampling driven by an informed proposal scheme controlled by a joint probability model combining motion, appearance and interaction among detected regions. Proposed data association method is robust and efficient, capable of handling extreme conditions with very noisy detection

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Pattern Recognition, 2006. ICPR 2006. 18th International Conference on  (Volume:2 )

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