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Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise

  • Yuefeng Chen
  • Qing Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

Recently a category of tracking methods based on “tracking-by-detection” is widely used in visual tracking problem. Most of these methods update the classifier online using the samples generated by the tracker to handle the appearance changes. However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples. In this paper, we split the class labels into true labels and noise labels and model them by sparse representation. A novel dynamic classifier selection method, robust to noisy training data, is proposed. Moreover, we apply the proposed classifier selection algorithm to visual tracking by integrating a part based online boosting framework. We have evaluated our proposed method on 12 challenging sequences involving severe occlusions, significant illumination changes and large pose variations. Both the qualitative and quantitative evaluations demonstrate that our approach tracks objects accurately and robustly and outperforms state-of-the-art trackers.

Keywords

Class Label Sparse Representation Appearance Model Visual Tracking Multiple Instance Learning 
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 2013

Authors and Affiliations

  • Yuefeng Chen
    • 1
  • Qing Wang
    • 1
  1. 1.School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anP.R. China

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