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Discrete-continuous optimization for multi-target tracking

By Anton Andriyenko, Konrad Schindler and Stefan Roth


The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, i.e. assigning image observations to the appropriate target; (ii) the naturally continuous problem of trajectory estimation, i.e. recovering the trajectories of all targets. To go beyond simple greedy solutions for data association, recent approaches often perform multi-target tracking using discrete optimization. This has the disadvantage that trajectories need to be pre-computed or represented discretely, thus limiting accuracy. In this paper we instead formulate multi-target tracking as a discrete continuous optimization problem that handles each aspect in its natural domain and allows leveraging powerful methods for multi-model fitting. Data association is performed using discrete optimization with label costs, yielding near optimality. Trajectory estimation is posed as a continuous fitting problem with a simple closed-form solution, which is used in turn to update the label costs. We demonstrate the accuracy and robustness of our approach with state-of-the art performance on several standard datasets

Topics: computer vision, people tracking, Markov random fields (MRF), optimization, Forschungsgruppe Visual Inference (VINF)
Year: 2012
DOI identifier: 10.1109/CVPR.2012.6247893
OAI identifier:
Provided by: Fraunhofer-ePrints
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