Kernel sparse representation for object tracking

Object tracking is a challenging problem to develop an effective model, which can handle appearance change caused by illumination change, occlusion, and motion blur. In this paper, we propose an online tracking algorithm with kernel sparse representation, local image patches of a target are represented by their sparse codes schemes with an over-complete dictionary, and online classifier is learned to discriminate the target. To improve robustness of the algorithm and the performance of the classifier, kernel function is applied on the sparse representation. In addition to, we propose a simple yet effective method for dictionary update. Experiments on challenging image sequences show that the proposed algorithm performs favorably against several state-of-the-art methods.