Efficient feature aided multi-object tracking in video surveillance

Object tracking in video surveillance is often restrained by the requirement of real time processing. In this paper, the object tracking problem is approached as two independent processes. At each video frame, multi-object information is approximately represented by a set of rectangle patches as the result of object detection and the data set is converted to a set of virtual location measurements as well as the associated feature measurements with assumption that location and feature measurements are independent. In the consequent process, a feature aided integrated probabilistic data association type multi-object tracker is used to recursively update the posterior densities of the objects estimated frame by frame based on the virtual measurements obtained from the former process. The uncertainty of the converted virtual measurements largely depends on the object detection techniques used and it may be handled in the later process by the tracker. Our results demonstrated that the feature aided data association technique can resolve the uncertainty due to the imperfect of virtual measurement process. Furthermore, the required computational overhead is considerably less than those of pixel-wise based approaches.