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Adaptive sampling for UAV tracking

  • Yong Wang
  • Xinbin LuoEmail author
  • Lu Ding
  • Shan Fu
  • Shiqiang Hu
Original Article
  • 48 Downloads

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.

Keywords

UAV tracking Structured support vector machine Object proposals Edge boxes Local kernel feature 

Notes

Compliance with ethical standards

Conflict of interest

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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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  3. 3.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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