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Dual-regression model for visual tracking
Abstract and Figures
Existing regression based tracking methods built on correlation filter model or convolution modeldo not take both accuracy and robustness into account at the same time. In this paper, we pro-pose a dual regression framework comprising a discriminative fully convolutional module and a fine-grained correlation filter component for visual tracking. The convolutional module trainedin a classification manner with hard negative mining ensures the discriminative ability of the proposed tracker, which facilitates the handling of several challenging problems, such as drastic de-formation, distracters, and complicated backgrounds. The correlation filter component built onthe shallow features with fine-grained features enables accurate localization. By fusing these twobranches in a coarse-to-fine manner, the proposed dual-regression tracking framework achievesa robust and accurate tracking performance. Extensive experiments on the OTB2013, OTB2015,and VOT2015 datasets demonstrate that the proposed algorithm performs favorably against thestate-of-the-art methods.
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