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Visual tracking via online discriminative multiple instance metric learning

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Abstract

Motion object tracking is an important issue in computer vision. In this paper, a robust tracking algorithm based on multiple instance learning (MIL) is proposed. First, a coarse-to-fine search method is designed to reduce the computation load of cropping candidate samples for a new arriving frame. Then, a bag-level similarity metric is proposed to select the most correct positive instances to form the positive bag. The instance’s importance to bag probability is determined by their Mahalanobis distance. Furthermore, an online discriminative classifier selection method, which exploits the average gradient and average weak classifiers strategy to optimize the margin function between positive and negative bags, is presented to solve the suboptimal problem in the process of selecting weak classifiers. Experimental results on challenging sequences show that the proposed method is superior to other compared methods in terms of both qualitative and quantitative assessments.

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Acknowledgements

This work was supported by the China Astronautic Science and Technology Innovation Foundation under Grant No. CASC201104, China Aviation Science Fund Project under Grant No. 2012ZC53043 and NSFC under 71471119.The authors would like to thank the valuable comments from the reviewers and editors.

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Authors and Affiliations

  1. Department of Automation, Northwestern Polytechinical University, Xi’an, Shannxi, China

    Honghong Yang & Shiru Qu

  2. College of Economics and Center for Finance and Accounting Research, Shenzhen University, Shenzhen, Guangdong, China

    Zunxin Zheng

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  1. Honghong Yang
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  2. Shiru Qu
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  3. Zunxin Zheng
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Correspondence to Zunxin Zheng.

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Yang, H., Qu, S. & Zheng, Z. Visual tracking via online discriminative multiple instance metric learning. Multimed Tools Appl 77, 4113–4131 (2018). https://doi.org/10.1007/s11042-017-4498-z

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