In this letter, we present a novel online tracking method based on sparse representation. In contrast to existing “sparse representation”-based tracking algorithms, this work adopts the sparse representation method to construct both object and state models. The tracked object can be sparsely represented by a series of object templates, and also can be sparsely represented by candidate samples in the current frame. Furthermore, we propose a unified objective function to integrate object and state models, and cast the tracking problem as an optimization problem that can be solved in an iteration manner. Finally, we compare the proposed tracker with nine state-of-the-art tracking methods by using some challenging image sequences. Both qualitative and quantitative evaluations demonstrate that our tracker achieves favorable performance in terms of both accuracy and speed.