A fragment based scale adaptive tracker with partial occlusion handling

We propose a fragment based algorithm for efficient target tracking under significant scale variation and partial occlusion. In contrast, none of the previous multiple part or fragment based algorithms are both scale adaptive and robust to partial occlusion. In our algorithm, the target is divided into a number of overlapping image fragments. Their color histograms are compared with those of candidate fragments within the neighborhood in subsequent frames. The candidate fragments with maximum histogram similarity with the template fragments contribute towards selection of location of best match within that neighborhood. Its position is further calibrated by using a localization method. We implement a systematic scale adaptive tracking scheme which is robust to significant changes in the target size. Extensive experimental results based on real life sequences and test datasets are presented which demonstrate the excellent tracking accuracy achieved by our algorithm at real time.