Extended MHT algorithm for multiple object tracking

In this paper, we propose an improved efficient MHT algorithm integrated with HSV-LBP appearance and repulsion-inertia model for multi-object tracking. Simultaneously tracking multiple objects is critical to video content analysis and virtual reality. The main issues we want to address in this paper are integration of video image patch information into data association and ambiguous observations caused by objects in close proximity. A likelihood function of HSV-LBP histogram with strategy of template updating is constructed. A repulsion-inertia model is adopted to explore more useful information from ambiguous detections. Experimental results show that the proposed approach generates better trajectories with less missing objects and identity switches.