Object tracking via a cooperative appearance model

Object tracking has been wildly used in security monitoring, traffic control, medical imaging and other fields. Conventional algorithms design local and holistic appearance model for effectively tracking. However, the performance of algorithms decreases in the complex scenes, including deformation and background cluster, partial or full occlusion and so on. In this paper, an object-tracking algorithm via a cooperative appearance model is proposed. Considering the visual characteristics of human eyes, we propose the impact region with different impact factors. The impact regions are defined as the regions which play different effects in making decision. The pixels in the regions with different distance from the target center will have different importance. We divide the impact regions into the significant impact region, the sub-impact region, and the non-impact region. The cooperative appearance model uses local collaborative representation to rectify holistic representation with impact regions. In local representation, positive and negative dictionary are derived from the candidates of video frames. The candidates are segmented into non-overlapping sub-blocks, and the sub-block responses of each candidate are obtained based on a collaborative dictionary. In holistic representation, the candidates are represented sparsely to obtain the total reconstruction error. The tracking result is decided by combing the sub-block responses in local representation and the reconstruction error in holistic representation with impact regions. The experimental results show that the proposed algorithm has performed well on deformation and illumination variation, partial or full occlusion, scale variation and background cluster compared with the state-of-the-art algorithms.