Online Multiple View Tracking: Targets Association Across Cameras

Abstract : Most multiple object tracking algorithms relying on a single view have failed to follow the trajectories of targets when they have been completely hidden by obstacles. In this paper, we introduce a novel method of collaborative tracking in a synchronized overlapping cameras network. We propose an efficient target association method between cameras based on the tracking results of each target on each view. Our framework naturally handles obstacle occlusions and mutual target occlusions. We implemented our multiple object tracking algorithm by Decision Making algorithm [30] on each view. The tracking outcomes on each camera are collected and associated into targets. The feedback from the central association helps the individual cameras in tracking hidden targets, even in the case of complete occlusion. We use the standard MOT metric to validate our method. The experimental results on each view show that the multiple view tracking system outperforms the single view ones. The source code will be available publicly.
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Quoc Cuong Le, Donatello Conte, Moncef Hidane. Online Multiple View Tracking: Targets Association Across Cameras. 6th Workshop on Activity Monitoring by Multiple Distributed Sensing (AMMDS 2018), Sep 2018, Newcastle, United Kingdom. ⟨hal-01880374⟩

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