Visual tracking via context-aware local sparse appearance model

Most existing local sparse trackers are prone to drifting away as they do not make use of discriminative information of local patches. In this paper, we propose an effective context-aware local sparse appearance model to alleviate the drift problem caused by background clutter and occlusions. First, considering that different local patches should have different impacts on the likelihood computation, we present a novel Impact Allocation Strategy (IAS) with integration of the spatial-temporal context. Varying positive impact factors are adaptively assigned to different local patches based on their ability distinguishing the spatial context, which provides discriminative information to prevent the tracker from drifting. Furthermore, we exploit temporal context to introduce some historical information for more accurate locating. Second, we present a new patch-based dictionary update method being able to update each patch independently with the validation of effectiveness. On the one hand, we introduce sparsity concentration index to check whether the local patch to be updated is a valid local patch from the target object. On the other hand, spatial context is further employed to eliminate the effect of the background. Experimental results show the superiority and competitiveness of the proposed method on the benchmark data set compared to other state-of-the-art algorithms.