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Semi-supervised On-Line Boosting for Robust Tracking

  • Helmut Grabner
  • Christian Leistner
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)

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.

Keywords

Feature Selection Unlabeled Data Tracking Loop Robust Tracking Unlabeled Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Helmut Grabner
    • 1
    • 2
  • Christian Leistner
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyAustria
  2. 2.Computer Vision LaboratoryETH ZurichSwitzerland

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