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Global data association for multi-object tracking using network flows

9 years 4 months ago
Global data association for multi-object tracking using network flows
We propose a network flow based optimization method for data association needed for multiple object tracking. The maximum-a-posteriori (MAP) data association problem is mapped into a cost-flow network with a non-overlap constraint on trajectories. The optimal data association is found by a min-cost flow algorithm in the network. The network is augmented to include an Explicit Occlusion Model(EOM) to track with long-term inter-object occlusions. A solution to the EOM-based network is found by an iterative approach built upon the original algorithm. Initialization and termination of trajectories and potential false observations are modeled by the formulation intrinsically. The method is efficient and does not require hypotheses pruning. Performance is compared with previous results on two public pedestrian datasets to show its improvement.
Li Zhang, Yuan Li, Ramakant Nevatia
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where CVPR
Authors Li Zhang, Yuan Li, Ramakant Nevatia
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