One-class support vector machine-assisted robust tracking

Keren Fu; Chen Gong; Yu Qiao; Jie Yang; Irene Yu-Hua Gu

doi:10.1117/1.JEI.22.2.023002

8 April 2013 One-class support vector machine-assisted robust tracking

Keren Fu, Chen Gong, Yu Qiao, Jie Yang, Irene Yu-Hua Gu

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 2, 023002 (April 2013). https://doi.org/10.1117/1.JEI.22.2.023002

Abstract

Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. We argue that tracking may be regarded as one-class problem, which avoids gathering limited negative samples for background description. Inspired by the fact the positive feature space generated by one-class support vector machine (SVM) is bounded by a closed hyper sphere, we propose a tracking method utilizing one-class SVMs that adopt histograms of oriented gradient and 2bit binary patterns as features. Thus, it is called the one-class SVM tracker (OCST). Simultaneously, an efficient initialization and online updating scheme is proposed. Extensive experimental results prove that OCST outperforms some state-of-the-art discriminative tracking methods that tackle the problem using binary classifiers on providing accurate tracking and alleviating serious drifting.

Citation Download Citation

Keren Fu, Chen Gong, Yu Qiao, Jie Yang, and Irene Yu-Hua Gu "One-class support vector machine-assisted robust tracking," Journal of Electronic Imaging 22(2), 023002 (8 April 2013). https://doi.org/10.1117/1.JEI.22.2.023002

Published: 8 April 2013

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