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Robust Object Tracking with Occlusion Handling based on Local Sparse Representation Hainan

Sparse representation has been successfully applied to visual tracking to find the target with the minimum reconstruction error from the target templates subspace. Traditional sparsity-based trackers handle corruptions and occlusions of the observation by introducing a set of trivial templates. However, the performance is not so satisfactory in practice. It is because the trivial templates unable to model heavy occlusions effectively, and the likelihood computation and the template update processes do not take full advantage of the occlusion information. In this paper, we propose a novel tracking method taking advantage of local sparse representation to detect occlusions during the tracking sequence. In our method, the target is divided into local patches. We analyze the spatial distribution of the samples employed by the local sparse representation, and determine the occlusion state for each patch respectively. The occluded patches are disregard, only the unoccluded ones are considered for reconstruction and likelihood computation. In addition, a dynamic template update strategy with occlusion handling is introduced to alleviate the drift problem. Experiments on challenging video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.