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Coupling Semi-supervised Learning and Example Selection for Online Object Tracking

  • Min YangEmail author
  • Yuwei Wu
  • Mingtao Pei
  • Bo Ma
  • Yunde Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9006)

Abstract

Training example collection is of great importance for discriminative trackers. Most existing algorithms use a sampling-and-labeling strategy, and treat the training example collection as a task that is independent of classifier learning. However, the examples collected directly by sampling are not intended to be useful for classifier learning. Updating the classifier with these examples might introduce ambiguity to the tracker. In this paper, we introduce an active example selection stage between sampling and labeling, and propose a novel online object tracking algorithm which explicitly couples the objectives of semi-supervised learning and example selection. Our method uses Laplacian Regularized Least Squares (LapRLS) to learn a robust classifier that can sufficiently exploit unlabeled data and preserve the local geometrical structure of feature space. To ensure the high classification confidence of the classifier, we propose an active example selection approach to automatically select the most informative examples for LapRLS. Part of the selected examples that satisfy strict constraints are labeled to enhance the adaptivity of our tracker, which actually provides robust supervisory information to guide semi-supervised learning. With active example selection, we are able to avoid the ambiguity introduced by an independent example collection strategy, and to alleviate the drift problem caused by misaligned examples. Comparison with the state-of-the-art trackers on the comprehensive benchmark demonstrates that our tracking algorithm is more effective and accurate.

Keywords

Object Tracking Unlabeled Data Classifier Learning Appearance Variation Label Noise 
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.

Notes

Acknowledgement

This work was supported in part by the Natural Science Foundation of China (NSFC) under grant NO. 61203291, the 973 Program of China under grant NO. 2012CB720000, the Specialized Research Fund for the Doctoral Program of Higher Education of China (20121101120029), and the Specialized Fund for Joint Building Program of Beijing Municipal Education Commission.

Supplementary material

336669_1_En_31_MOESM1_ESM.pdf (1.1 mb)
Supplementary material (pdf 1,118 KB)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Min Yang
    • 1
    Email author
  • Yuwei Wu
    • 1
  • Mingtao Pei
    • 1
  • Bo Ma
    • 1
  • Yunde Jia
    • 1
  1. 1.Beijing Laboratory of Intelligent Information TechnologySchool of Computer Science, Beijing Institute of TechnologyBeijingChina

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