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2007
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Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association

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Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association
We propose a framework for general multiple target tracking, where the input is a set of candidate regions in each frame, as obtained from a state of the art background learning, and the goal is to recover trajectories of targets over time from noisy observations. Due to occlusions by targets and static objects, noisy segmentation and false alarms, one foreground region may not correspond to one target faithfully. Therefore the one-to-one assumption used in most data association algorithm is not always satisfied. Our method overcomes the one-to-one assumption by formulating the visual tracking problem in terms of finding the best spatial and temporal association of observations, which maximizes the consistency of both motion and appearance of trajectories. To avoid enumerating all possible solutions, we take a Data Driven Markov Chain Monte Carlo (DD-MCMC) approach to sample the solution space efficiently. The sampling is driven by an informed proposal scheme controlled by a joint ...
Qian Yu, Gérard G. Medioni, Isaac Cohen
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where CVPR
Authors Qian Yu, Gérard G. Medioni, Isaac Cohen
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