Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking

Recently, sparse based learning methods have attracted much attention in robust visual tracking due to their effectiveness and promising tracking results. By representing the target object sparsely, utilising only a few adaptive dictionary templates, in this paper, we introduce a new particle filter based tracking method, in which we aim to capture the underlying structure among the particle samples using the proposed similarity graph in a Laplacian group sparse framework, such that the tracking results can be improved. Furthermore, in our tracker, particles contribute with different probabilities in the tracking result with respect to their relative positions in a given frame in regard to the current target object location. In addition, since the new target object can be well modelled by the most recent tracking results, we prefer to utilise the particle samples that are highly associated to the preceding tracking results. We demonstrate that the proposed formulation can be efficiently solved using the Accelerated Proximal method with just a small number of iterations. The proposed approach has been extensively evaluated on 12 challenging video sequences. Experimental results compared to the state-of-the-art methods demonstrate the merits of the proposed tracker.