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2006
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Boosted Markov Chain Monte Carlo Data Association for Multiple Target Detection and Tracking

10 years 3 months ago
Boosted Markov Chain Monte Carlo Data Association for Multiple Target Detection and Tracking
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.
Bo Wu, Gérard G. Medioni, Isaac Cohen, Qian
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Bo Wu, Gérard G. Medioni, Isaac Cohen, Qian Yu
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