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Sparse coding ( SC) based visual tracking ( l1?tracker) is gaining increasing attention, and many related algorithms are developed. In these algorithms, each candidate region is sparsely represented as a set of target tem?plates. However, the structure connecting these candidate regions is usually ignored. Lu proposed an NLSSC?tracker with non?local self?similarity sparse coding to address this issue, which has a high computational cost. In this study, we propose an Euclidean local?structure constraint based sparse coding tracker with a smoothed Euclidean local structure. With this tracker, the optimization procedure is transformed to a small?scale l1?optimization problem, sig?nificantly reducing the computational cost. Extensive experimental results on visual tracking demonstrate the effectiveness and efficiency of the proposed algorithm.