Discrete-continuous optimization for multi-target tracking
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Date
2012
Authors
Milan, A.
Schindler, K.
Roth, S.
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Conference paper
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Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition, held in Providence, Rhode Island, 16-21 June, 2012 / pp.1926-1933
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Anton Andriyenko, Konrad Schindler, Stefan Roth
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IEEE Conference on Computer Vision and Pattern Recognition (25th : 2012 : Providence, Rhode Island)
Abstract
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.
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©2012 IEEE