An analytical formulation of global occlusion reasoning for multi-target tracking

We present a principled model for occlusion reasoning in complex scenarios with frequent inter-object occlusions, and its application to multi-target tracking. To compute the putative overlap between pairs of targets, we represent each target with a Gaussian. Conveniently, this leads to an analytical form for the relative overlap - another Gaussian - which is combined with a sigmoidal term for modeling depth relations. Our global occlusion model bears several advantages: Global target visibility can be computed efficiently in closed-form, and varying degrees of partial occlusion can be naturally accounted for. Moreover, the dependence of the occlusion on the target locations - i.e. the gradient of the overlap - can also be computed in closed-form, which makes it possible to efficiently include the proposed occlusion model in a continuous energy minimization framework. Experimental results on seven datasets confirm that the proposed formulation consistently reduces missed targets and lost trajectories, especially in challenging scenarios with crowds and severe inter-object occlusions.