Share Email Print

Journal of Electronic Imaging

Large margin classifier-based ensemble tracking
Author(s): Yuru Wang; Qiaoyuan Liu; Minghao Yin; ShengSheng Wang
Format Member Price Non-Member Price
PDF $20.00 $25.00

Paper 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.

Paper Details

Date Published: 12 July 2016
PDF: 11 pages
J. Electron. Imag. 25(4) 043006 doi: 10.1117/1.JEI.25.4.043006
Published in: Journal of Electronic Imaging Volume 25, Issue 4
Show Author Affiliations
Yuru Wang, Northeast Normal Univ. (China)
Qiaoyuan Liu, Northeast Normal Univ. (China)
Minghao Yin, Northeast Normal Univ. (China)
ShengSheng Wang, Jilin Univ. (China)

© SPIE. Terms of Use
Back to Top