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Abstract
Recently, on-line adaptation of binary classifiers for tracking have been investigated. On-line learning allows for simple classifiers since only the current view of the object from its surrounding background needs to be discriminiated. However, on-line adaption faces one key problem: Each update of the tracker may introduce an error which, finally, can lead to tracking failure (drifting). The contribution of this paper is a novel on-line semi-supervised boosting method which significantly alleviates the drifting problem in tracking applications. This allows to limit the drifting problem while still staying adaptive to appearance changes. The main idea is to formulate the update process in a semi-supervised fashion as combined decision of a given prior and an on-line classifier. This comes without any parameter tuning. In the experiments, we demonstrate real-time tracking of our SemiBoost tracker on several challenging test sequences where our tracker outperforms other on-line tracking methods.
This work has been supported by the Austrian Joint Research Project Cognitive Vision under projects S9103-N04 and S9104-N04, the FFG project EVis (813399) under the FIT-IT program and the Austrian Science Fund (FWF) under the doctoral program Confluence of Vision and Graphics W1209.
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Authors and Affiliations
Institute for Computer Graphics and Vision, Graz University of Technology, Austria
Helmut Grabner, Christian Leistner & Horst Bischof
Computer Vision Laboratory, ETH Zurich, Switzerland
Helmut Grabner
- Helmut Grabner
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- Christian Leistner
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- Horst Bischof
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Computer Science Department, University of Illinois at Urbana Champaign, 3310 Siebel Hall, Urbana, IL 61801, USA
David Forsyth
Department of Computing, Oxford Brookes University, OX33 1HX, Wheatley, Oxford, UK
Philip Torr
Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, Oxford, UK
Andrew Zisserman
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Grabner, H., Leistner, C., Bischof, H. (2008). Semi-supervised On-Line Boosting for Robust Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_19
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