Efficient visual object tracking with online nearest neighbor classifier

  • Authors:
  • Steve Gu;Ying Zheng;Carlo Tomasi

  • Affiliations:
  • Department of Computer Science, Duke University;Department of Computer Science, Duke University;Department of Computer Science, Duke University

  • Venue:
  • ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
  • Year:
  • 2010

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Abstract

A tracking-by-detection framework is proposed that combines nearest-neighbor classification of bags of features, efficient subwindow search, and a novel feature selection and pruning method to achieve stability and plasticity in tracking targets of changing appearance. Experiments show that near-frame-rate performance is achieved (sans feature detection), and that the state of the art is improved in terms of handling occlusions, clutter, changes of scale, and of appearance. A theoretical analysis shows why nearest neighbor works better than more sophisticated classifiers in the context of tracking.