Robust Visual Tracking via Sparse Representation Under Subclass Discriminant Constraint

In this paper, we propose a method for visual tracking based on local sparse representation. Image patches from the object and the background are split into image blocks to construct local representations. Within the subclass discriminant framework, a discriminative subspace is learned to distinguish the object image blocks from the background image blocks while preserving their multimodal structure. A dictionary is constructed using the centers of the object subclasses. With this dictionary, sparse coding is implemented on the projected vectors corresponding to the image blocks, and the sparse coefficients are concatenated to obtain a local sparse code as the feature that represents the image patch. Considering the subclass discriminant constraint and the sparsity constraint imposed on the sparse coding, the subspace learning and sparse representation problems are converted into a joint optimization problem with respect to a transformation matrix and sparse coefficients. To enhance the tracking accuracy, two dictionaries are devised, one to incorporate the original observations of the target and the other to incorporate the latest observations, thereby providing two templates to characterize the appearance of the target. Histogram intersection over the local sparse codes provides an evaluation of the confidence. Finally, the candidate with the maximal confidence is selected as the object image patch. Compared with several state-of-the-art algorithms, our method demonstrates a superior performance when applied to challenging sequences.