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Adaptive sampling for UAV tracking

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Abstract

Unmanned aerial vehicle (UAV)-based target tracking is a long-standing problem in UAV applications. In this paper, we develop a local kernel feature to encode properties of UAV tracking object. Meanwhile, object proposals can provide a reliable prior knowledge to identify tracking target being an object or not. Therefore, we propose to integrate detection proposal method into a tracking by detection framework. More specifically, we adopt edge box proposals and random samplings as training examples and then train these examples for tracking task. The structured support vector machine is employed to implement training and detecting procedure. To reveal the effectiveness of our method, experiment is performed on the UAV123 benchmark dataset. Among state-of-the-art methods, our method achieves comparable results.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Yong Wang, Lu Ding & Shiqiang Hu

  2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xinbin Luo & Shan Fu

  3. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Yong Wang

Authors
  1. Yong Wang
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  2. Xinbin Luo
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  3. Lu Ding
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  4. Shan Fu
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  5. Shiqiang Hu
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Correspondence to Xinbin Luo.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

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This paper is jointly supported by the National Natural Science Foundation of China No. 61374161, China Aviation Science Foundation 20142057006, Chinese special research program of a large civil aircraft under the Grant MJZ-2016-S-04.

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Wang, Y., Luo, X., Ding, L. et al. Adaptive sampling for UAV tracking. Neural Comput & Applic 31, 5029–5043 (2019). https://doi.org/10.1007/s00521-018-03996-8

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