Object Tracking via Dual Linear Structured SVM and Explicit Feature Map

Jifeng Ning, Jimei Yang, Shaojie Jiang, Lei Zhang, Ming-Hsuan Yang; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4266-4274

Abstract


Structured support vector machine (SSVM) based methods has 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 low 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 in a multiscale manner 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.

Related Material


[pdf]
[bibtex]
@InProceedings{Ning_2016_CVPR,
author = {Ning, Jifeng and Yang, Jimei and Jiang, Shaojie and Zhang, Lei and Yang, Ming-Hsuan},
title = {Object Tracking via Dual Linear Structured SVM and Explicit Feature Map},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}