Object tracking via sparse representation of DCT features

In this paper, a new algorithm is proposed for the object tracking in the complex background, in which discrete cosine transform (DCT) features of image are used for expressing candidate objects and the templates, casting tracking as a sparse approximation problem. Firstly, the location of object is labeled and templates of DCT features are constructed in the first frame. Secondly, the candidate objects are searched by random particles in the next frame and the sparsity is achieved by solving an h-regularized least-squares problem. The candidate with the smallest object templates projection error is chosen as the tracking object. Finally, the DCT feature templates are updated using the most recent tracking results to capture changes of the object appearance. Experimental results show the effectiveness of the proposed method comparing with other tracking algorithms.