A Multiple Hypothesis Tracking Method with Fragmentation Handling

In this paper, we present a new multiple hypotheses tracking (MHT) approach. Our tracking method is suitable for online applications, because it labels objects at every frame and estimates the best computed trajectories up to the current frame. In this work we address the problems of object merging and splitting (occlusions) and object fragmentations. Object fragmentation resulting from imperfect background subtraction can easily be confused with splitting objects in a scene, especially in close range surveillance applications. This subject is not addressed in most MHT methods. In this work, we propose a framework for MHT which distinguishes fragmentation and splitting using their spatial and temporal characteristics and by generating hypotheses only for splitting cases using observation in later frames. This approach results in a more accurate data association and a reduced size of the hypothesis graph. Our tracking method is evaluated with various indoor videos.