Context-aware real-time tracking in sparse representation framework

Real-time object tracking is a difficult task in unconstrained environment due to the variations in factors such as pose, size, illumination, partial occlusion and motion blur. In this paper, we propose a novel approach based on local sparse representation for robust object tracking to address the above issues. In the proposed approach, a search window, including the object and the surrounding information is used to create dictionary of overlapping patches. This context-based method is used to discriminate the confusing patches of the foreground with those in the background. Candidate patches are sparsely represented in the dictionary and foreground/background classification is done by computing the confidence map based on the distribution of sparse coefficients. Pyramidal structure of the object window that depicts object at different scales is used to create a dictionary that can handle scale changes. Object is localized by seeking the mode of the confidence map. A suitable dictionary update strategy is used to alleviate the drift problem during tracking. Numerous experiments on challenging videos demonstrate that the proposed tracker outperforms several state-of-the-art algorithms. The proposed approach tracks at a high processing speed and is suitable for real-time applications.