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- W134869576 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." @default.
- W134869576 created "2016-06-24" @default.
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- W134869576 date "2011-01-01" @default.
- W134869576 modified "2023-09-24" @default.
- W134869576 title "Visual Tracking Using Online Semi-supervised Learning" @default.
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- W134869576 doi "https://doi.org/10.1007/978-3-642-21593-3_41" @default.
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