Visual tracking plays an important role in computer vision research in these years. Multi-kernel correlation filter has demonstrated its outstanding advantage via introducing high level representation from multi-kernel. However, the unskillful selection of multi-kernel inevitably brings redundancy and noise within learning and updating procedure, which significantly affects the accuracy of tracking. A large margin multi-kernel tensor correlation filter for visual tracking (LMKCF) is proposed in this paper. The LMKCF mainly mitigates the redundancy and noise of multi-kernel correlation filter in learning and updating from two aspects with the low rank tensor learning to establishes a prospective learning and updating strategy. And the optimization problem can be solved effectively by the alternating direction method of multipliers (ADMM) method. Last, we validate the proposed tracker with the multi-kernel representations based on OTB benchmark to demonstrate the superiority of the method.
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