On-line Semi-supervised Multiple-Instance Boosting

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On-line Semi-supervised Multiple-Instance Boosting
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deliver excellent results and run in real-time they also tend to drift in case of wrong updates during the self-learning process. Recent approaches tackled this problem by formulating tracking-by-detection as either one-shot semi-supervised learning or multiple instance learning. Semi-supervised learning allows for incorporating priors and is more robust in case of occlusions while multiple-instance learning resolves the uncertainties where to take positive updates during tracking. In this work, we propose an on-line semi-supervised learning algorithm which is able to combine both of these approaches into a coherent framework. This leads to more robust results than applying both approaches separately. Additionally, we introduce a combined loss that simultaneously uses labeled and unlabeled samples, which makes ou...
Bernhard Zeisl, Christian Leistner, Amir Saffari,
Added 21 Apr 2010
Updated 16 Jul 2010
Type Conference
Year 2010
Where CVPR
Authors Bernhard Zeisl, Christian Leistner, Amir Saffari, Horst Bischof
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