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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Visual Tracking Using Online Semi-supervised Learning
verfasst von : Meng Gao, Huaping Liu, Fuchun Sun
Erschienen in: Image Analysis and Recognition
Verlag: Springer Berlin Heidelberg
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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.