Tracking multiple objects in the presence of articulated and occluded motion

This paper presents a novel approach to the tracking of multiple articulate objects in the presence of occlusion in moderately complex scenes. Most conventional tracking algorithms work well when only one object is tracked at a time. However, when multiple objects must be tracked simultaneously, significant computation is often introduced in order to handle occlusion and to calculate the appropriate region correspondence between successive frames. We introduce a near real-time solution to this problem by using a probabilistic mixing of low-level features and components. The algorithm mixes coarse motion estimates, change detection information, and unobservable predictions to create accurate trajectories of moving objects. We implement this multi-feature mixing strategy within the context of a video surveillance system using a modified Kalman filtering mechanism. Experimental results demonstrate the efficacy of the proposed tracking and surveillance system.