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.
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