Multitarget tracking using multifeature model with acceleration feature

Multitarget tracking in video surveillance is challenging because the appearance features of the target are often unreliable in complicated scenes. To solve the problem, we propose a multitarget tracking method using multifeature model with acceleration feature. First, an acceleration feature descriptor is derived from the histograms of the optical flow features according to the background difference. Our approach filters and normalizes the descriptors of consecutive frames to establish acceleration feature models. Then, the multifeature models of target templates are initialized by combining acceleration and multiple spatial feature models. Second, we implement data association based on the tracklet confidence by integrating the acceleration and multiple spatial feature affinities. As a result, the optimal associated pairs between target tracklets and detections are solved by the Hungarian algorithm. Finally, our tracking system updates the multifeature models of target templates online depending on the reliability of the tracklets, and the trajectories of multiple targets are output. Experiments conducted on the challenging multiple object tracking benchmark confirm the effectiveness and superiority of the proposed method.