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One-class support vector machine-assisted robust tracking

Keren Fu ; Chen Gong ; Yu Qiao ; Jie Yang ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling)
Journal of Electronic Imaging (1017-9909). Vol. 22 (2013), 2, p. 11.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: visual object tracking, support vector machine, detection and tracking, multiple instant learning



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Denna post skapades 2013-05-08. Senast ändrad 2013-09-03.
CPL Pubid: 176704

 

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Institutionen för signaler och system, Signalbehandling (1900-2017)

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