Online Visual Multi-Object Tracking via Labeled Random Finite Set Filtering

18 Nov 2016Du Yong KimBa-Ngu VoBa-Tuong Vo

This paper proposes an online visual multi-object tracking algorithm using a top-down Bayesian formulation that seamlessly integrates state estimation, track management, clutter rejection, occlusion and mis-detection handling into a single recursion. This is achieved by modeling the multi-object state as labeled random finite set and using the Bayes recursion to propagate the multi-object filtering density forward in time... (read more)

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