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Aiming to the challenge of occlusion during tracking, this paper proposes an anti-occlusion tracking based on joint confidence. Under the framework of the kernel correlation filter (KCF) tracking, the dimension of the feature is extended to construct a robust target appearance model, and the size of the target is estimated during the tracking process. We first judge whether occlusion occurs or not by the measurement by combining the maximum of the detection response map with the average peak correlation energy, then design the corresponding anti-interference tracking strategy. If the occlusion does not occur during the tracking process, the KCF tracking is performed, otherwise, re-detection is introduced to locate the target position, and the region corresponding to the re-detection is added to the regulation term of the KCF for context learning. The fusion of the filter template before occlusion and the context model learned during occlusion is used to locate the target and to update the model. Experimental evaluations on the datasets OTB2013, OTB100 and TC128 show that compared with the state-of-the-art algorithms such as KCF, Siamese and other algorithms, our proposed algorithm has stronger robustness and higher tracking accuracy when occlusion occurs.

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