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One-class support vector machine-assisted robust tracking

[+] Author Affiliations
Keren Fu

Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, Shanghai, China

Ministry of Education of China, Key Laboratory of System Control and Information Processing, Shanghai 200240, China

Chen Gong

Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, Shanghai, China

Ministry of Education of China, Key Laboratory of System Control and Information Processing, Shanghai 200240, China

Yu Qiao

Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, Shanghai, China

Ministry of Education of China, Key Laboratory of System Control and Information Processing, Shanghai 200240, China

Jie Yang

Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, Shanghai, China

Ministry of Education of China, Key Laboratory of System Control and Information Processing, Shanghai 200240, China

Irene Yu-Hua Gu

Chalmers University of Technology, Department of Signals and Systems, Signal Processing Group, Gothenburg 41296, Sweden

J. Electron. Imaging. 22(2), 023002 (Apr 08, 2013). doi:10.1117/1.JEI.22.2.023002
History: Received November 22, 2012; Revised March 12, 2013; Accepted March 15, 2013
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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.

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© 2013 SPIE and IS&T

Citation

Keren Fu ; Chen Gong ; Yu Qiao ; Jie Yang and Irene Yu-Hua Gu
"One-class support vector machine-assisted robust tracking", J. Electron. Imaging. 22(2), 023002 (Apr 08, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.2.023002


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