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http://hdl.handle.net/10397/105697
Title: | Object tracking via dual linear structured SVM and explicit feature map | Authors: | Ning, J Yang, J Jiang, S Zhang, L Yang, MH |
Issue Date: | 2016 | Source: | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 26 June - 1 July 2016, Las Vegas, Nevada, p. 4266-4274 | Abstract: | Structured support vector machine (SSVM) based methods have demonstrated encouraging performance in recent object tracking benchmarks. However, the complex and expensive 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. | Publisher: | Institute of Electrical and Electronics Engineers | ISBN: | 978-1-4673-8850-4 | DOI: | 10.1109/CVPR.2016.462 | Rights: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication J. Ning, J. Yang, S. Jiang, L. Zhang and M. -H. Yang, "Object Tracking via Dual Linear Structured SVM and Explicit Feature Map," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 4266-427 is available at https://doi.org/10.1109/CVPR.2016.462. |
Appears in Collections: | Conference Paper |
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