Semi-Supervised On-line Boosting for Robust Tracking

Authors	Grabner Helmut, Leistner Christian, Bischof Horst
Appeared in	In Proceedings European Conference on Computer Vision (ECCV)
Date	2008
Abstract	Recently, on-line adaptation of binary classifers for tracking have been investigated. On-line learning allows for simple classiers 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 signifcantly 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 semisupervised fashion as combined decision of a given prior and an on-line classier. 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.
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