Robust Coding via L2-norm Regularization for Visual Tracking

Sparse representation based visual trackers are very computationally inefficient and prone to model drifting. To deal with these issues, a novel visual tracking method is proposed based on L2 -norm regularized robust coding. The proposed method solves the coding coefficient of candidate objects via robust coding based on L2-norm regularization, and it achieves visual tracking by taking weighted reconstruction errors of the candidate object as observation likelihood in particle filter framework. In addition, to adapt the changes of object appearance and avoid model drifting, an occlusion detection method for template update is proposed by investigating the weight matrix of current object estimated with L2-norm regularized robust coding. The experimental results on several challenging sequences show that the proposed method has better performance than that of the state-of-the-art tracker.