Robust tracking via patch-based appearance model and local background estimation

In this paper, to simultaneously address the tracker drift and occlusion problem, we propose a robust visual tracking algorithm via a patch-based adaptive appearance model driven by local background estimation. Inspired by human visual mechanisms (i.e., context-awareness and attentional selection), an object is represented with a patch-based appearance model, in which each patch outputs a confidence map during the tracking. Then, these confidence maps are combined via a robust estimator to finally get more robust and accurate tracking results. Moreover, we present a local spatial co-occurrence based background modeling approach to automatically estimate the local context background model of an interested object captured from a single camera, which may be stationary or moving. Finally, we utilize local background estimation to provide supervision to an analysis of possible occlusions and the adaption of patch-based appearance model of an object. Qualitative and quantitative experimental results on challenging videos demonstrate the robustness of the proposed method.