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Training Sequential On-line Boosting Classifiers for Visual Tracking

H. Grabner, J. Sochman, H. Bischof and J. Matas
International Conference on Pattern Recognition (ICPR'08)


On-line boosting allows to adapt a trained classifier to changing environmental conditions or to use sequentially available training data. Yet, two important problems in the on-line boosting training remain unsolved: (i) classifier evaluation speed optimization and, (ii) automatic classifier complexity estimation. In this paper we show how the on-line boosting can be combined with Wald's sequential decision theory to solve both of the problems. The properties of the proposed on-line WaldBoost algorithm are demonstrated on a visual tracking problem. The complexity of the classifier is changing dynamically depending on the difficulty of the problem. On average, a speedup of a factor of 5-10 is achieved compared to the non-sequential on-line boosting.

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  author = {H. Grabner and J. Sochman and H. Bischof and J. Matas},
  title = {Training Sequential On-line Boosting Classifiers for Visual Tracking},
  booktitle = {International Conference on Pattern Recognition (ICPR'08)},
  year = {2008},
  keywords = {}