Visual tracking using online semi-supervised learning

Authors:
Meng Gao;Huaping Liu;Fuchun Sun
Affiliations:
Shijiazhuang Tiedao University, Shijiazhuang, Hebei Province, P.R.China;Department of Computer Science and Technology, Tsinghua University, P.R.China and State Key Laboratory of Intelligent Technology and Systems, Beijing, P.R.China;Department of Computer Science and Technology, Tsinghua University, P.R.China and State Key Laboratory of Intelligent Technology and Systems, Beijing, P.R.China
Venue:
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Year:
2011

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Abstract

Since there does not exist labelled samples during tracking period, most existing classification-based tracking approaches utilize a "self-learning" to online update the classifier. This often results in drift problems. Recently, semi-supervised learning attracts a lot of attentions and is incorporated into the tracking framework which collects unlabelled samples and use them to enhance the robustness of the classifier. In this paper, we develop a gradient semi-supervised learning approaches for this application. During the tracking period, the semi-supervised technology is used to online update the classifier. Experimental evaluations demonstrate the effectiveness of the proposed approach.