Incremental subspace and probability mask constrained tracking in smart and autonomous systems

In this paper, we propose a novel incremental subspace and probability mask constrained sparse representation model in Smart and Autonomous Systems (SAS). In contrast to traditional sparse representation based tracking methods, the proposed model uses the trivial templates to model both reconstruction errors caused by sparse representation and the Eigen subspace representation. Besides, to alleviate the fact that the trivial templates can be activated to represent any image patch, we further propose to constrain the trivial templates with a probability mask. A unified objective function is proposed and a customized APG method is developed to effectively solve the optimization problem. In addition, a robust observation likelihood metric is proposed and a Bayes rule based color histogram model is proposed to construct the probability mask. Numerous qualitative and quantitative evaluations demonstrate that our tracker outperforms the state-of-the-art trackers in a wide range of tracking scenarios.