Multi-object Tracking with Explicit Reasoning about Occlusion

Abstract

Multi-object tracking in monocular video sequence is a challenging work when objects are occluded and objects' number is unknown or varies during tracking. In this paper, a multi-object parallel tracking method is proposed based on Bayesian framework. First, our method is designed to avoid huge amount of computation as required in multi-object joint tracking method. Second, our method can explicitly reason about occlusions, the depth ordering of interactive objects is inferred. We calculate the observation transition matrix to determine the movement transition between successive frames, given the object observations obtained in each frame. Each tracker could also collaborate with one another to decide which object is occluding and which is occluded when occlusion occurs. Our experiment results demonstrate that our method of using multiple trackers could automatically initialize and track multiple objects with varying numbers and occlusion.