Fragmentation handling for visual tracking

Object detection and tracking using background subtraction suffers from the fragmentation problem which means one object fragments into several blobs because of being similar with the reference image in color. In this paper, we build a visual tracking framework using background subtraction for object detection, and we address the association difficulty of blobs with objects caused by the fragmentation problem by two steps. We firstly cluster the blobs according to the boundary distances of them estimated by an approximating method proposed in this paper. Blobs clustered into the same blob-set are considered from the same object. Secondly, we consider blob-sets possibly from the same object if they exhibit coherent motion, since blobs of the same object may be clustered into different blob-sets if the object fragments severely. A background-matching method is proposed to determine whether two blob-sets exhibiting coherent motion are truly from the same object or from different objects. We test the proposed methods on several real-world video sequences. Quantitative and qualitative experimental results show that the proposed methods handle the problems caused by fragmentation effectively.