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Robust visual tracking via online semi-supervised co-boosting

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Abstract

This paper proposes a novel visual tracking algorithm via online semi-supervised co-boosting, which investigates the benefits of co-boosting (i.e., the integration of co-training and boosting) and semi-supervised learning in the online tracking process. Existing discriminative tracking algorithms often use the classification results to update the classifier itself. However, the classification errors are easily accumulated during the self-training process. In this paper, we employ an effective online semi-supervised co-boosting framework to update the weak classifiers built on two different feature views. In this framework, the pseudo-label and importance of an unlabeled sample are estimated based on the additive logistic regression for an integration of a prior model and an online classifier learned on one feature view, and then used to update the weak classifiers built on the other feature view. The proposed algorithm has a good ability to recover from drifting by incorporating prior knowledge of the object while being adaptive to appearance changes by effectively combining the complementary strengths of different feature views. Experimental results on a series of challenging video sequences demonstrate the superior performance of our algorithm compared to state-of-the-art tracking algorithms.

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Notes

  1. Code available at http://www.vision.ee.ethz.ch/boostingTrackers/download.html.

  2. Code available at http://www.vision.ee.ethz.ch/boostingTrackers/download.html.

  3. Code available at http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml.

  4. Code available at http://code.google.com/p/online-weighted-miltracker/.

  5. Video sequences available at http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml.

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Acknowledgments

This work is supported by Xiamen University of Technology High Level Talents Project (No. YKJ14020R), the National Natural Science Foundation of China (Nos. 61373147 and 61201359), and the Natural Science Foundation of Fujian Province (No. 2012J05126).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

    Si Chen & Shunzhi Zhu

  2. School of Information Science and Technology, Xiamen University, Xiamen, China

    Yan Yan

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  1. Si Chen
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  2. Shunzhi Zhu
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  3. Yan Yan
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Corresponding author

Correspondence to Shunzhi Zhu.

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Communicated by T. Mei.

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Chen, S., Zhu, S. & Yan, Y. Robust visual tracking via online semi-supervised co-boosting. Multimedia Systems 22, 297–313 (2016). https://doi.org/10.1007/s00530-015-0459-4

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