Object Tracking With Joint Optimization of Representation and Classification
We present a novel algorithm that exploits joint optimization of representation and classification for robust tracking in which the goal is to minimize the least-squares reconstruction errors and discriminative penalties with regularized constraints. In this formulation, an object is represented by the sparse coefficients of local patches based on an overcomplete dictionary, and a classifier is learned to discriminate the target object from the background. To locate the target object in each frame, we propose a deterministic approach to solve the optimization problem. We show that the proposed algorithm can be considered as a generalization of several tracking methods with effectiveness. To account for appearance change of the target and the background, the classifier is adaptively updated with new tracking results. Compared with the most recent tracking algorithms based on sparse representation, the proposed formulation has more discriminative power due to the use of background information and is much faster due to the use of deterministic optimization. Qualitative and quantitative experiments on a variety of challenging sequences show favorable performance of the proposed algorithm against several state-of-the-art methods.