Visual Tracking via Dual Linear Structured SVM and Explicit Feature Map

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Authors J. Ning, J. Yang, S. Jiang, L. Zhang and M-H Yang
Journal/Conference Name 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)
Paper Category
Paper Abstract Structured support vector machine (SSVM) based methods have demonstrated encouraging performance in recent object tracking benchmarks. However, the complex and ex-pensive optimization limits their deployment in real-world applications. In this paper, we present a simple yet efficient dual linear SSVM (DLSSVM) algorithm to enable fast learning and execution during tracking. By analyzing the dual variables, we propose a primal classifier update formula where the learning step size is computed in closed form. This online learning method significantly improves the robustness of the proposed linear SSVM with lower computational cost. Second, we approximate the intersection kernel for feature representations with an explicit feature map to further improve tracking performance. Finally, we extend the proposed DLSSVM tracker with multi-scale estimation to address the “drift” problem. Experimental results on large benchmark datasets with 50 and 100 video sequences show that the proposed DLSSVM tracking algorithm achieves state-of-the-art performance.
Date of publication 2016
Code Programming Language MATLAB

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