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Focusing on the problems of target deformation, occlusion, background interference and rotation, a robust video tracking method is proposed in this paper, which is based on the superpixels and dynamic graph matching. Firstly, to make the superpixels edge fit better and structure tighter, the local gradient feature is fused into the simple linear iterative clustering (SLIC) method. Secondly, the candidate target superpixels set is generated by Graph Cuts and to obtain more accurate foreground superpixels set, the LASVM classification results are fused into the Graph Cuts energy function. Thirdly, in order to make the proposed tracker more robust, the color local entropy is fused into the diagonal elements of the affinity matrix. Experiment results show that the proposed algorithm has strong robustness and better tracking accuracy.

Research on Dynamic Graph Target Tracking Method Fusing the Color Local Entropy