Large margin classifier-based ensemble tracking

Yuru Wang; Qiaoyuan Liu; Minghao Yin; ShengSheng Wang

doi:10.1117/1.JEI.25.4.043006

12 July 2016 Large margin classifier-based ensemble tracking

Yuru Wang, Qiaoyuan Liu, Minghao Yin, ShengSheng Wang

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 4, 043006 (July 2016). https://doi.org/10.1117/1.JEI.25.4.043006

Abstract

In recent years, many studies consider visual tracking as a two-class classification problem. The key problem is to construct a classifier with sufficient accuracy in distinguishing the target from its background and sufficient generalize ability in handling new frames. However, the variable tracking conditions challenges the existing methods. The difficulty mainly comes from the confused boundary between the foreground and background. This paper handles this difficulty by generalizing the classifier’s learning step. By introducing the distribution data of samples, the classifier learns more essential characteristics in discriminating the two classes. Specifically, the samples are represented in a multiscale visual model. For features with different scales, several large margin distribution machine (LDMs) with adaptive kernels are combined in a Baysian way as a strong classifier. Where, in order to improve the accuracy and generalization ability, not only the margin distance but also the sample distribution is optimized in the learning step. Comprehensive experiments are performed on several challenging video sequences, through parameter analysis and field comparison, the proposed LDM combined ensemble tracker is demonstrated to perform with sufficient accuracy and generalize ability in handling various typical tracking difficulties.

Citation Download Citation

Yuru Wang, Qiaoyuan Liu, Minghao Yin, and ShengSheng Wang "Large margin classifier-based ensemble tracking," Journal of Electronic Imaging 25(4), 043006 (12 July 2016). https://doi.org/10.1117/1.JEI.25.4.043006

Published: 12 July 2016

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