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Volume 49 Issue 1
Jan.  2023
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Journal of Beijing University of Aeronautics and Astronautics  > 2023  >  49(1): 53-65.
JIN G D，XUE Y L，TAN L N，et al. Aerial object tracking algorithm for UAVs based on dual-attention shuffling[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（1）：53-65 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0177
Citation: JIN G D，XUE Y L，TAN L N，et al. Aerial object tracking algorithm for UAVs based on dual-attention shuffling[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（1）：53-65 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0177
shu

Aerial object tracking algorithm for UAVs based on dual-attention shuffling

doi: 10.13700/j.bh.1001-5965.2021.0177

  • School of Nuclear Engineering，Rocket Force University of Engineering，Xi’an 710025，China

Funds:  National Natural Science Foundation of China (61673017,61403398)
More Information
  • Corresponding author: E-mail：641797825@qq.com
  • Received Date: 07 Apr 2021
  • Accepted Date: 28 May 2021
    • Available Online: 16 Jan 2023
  • Publish Date: 16 Jun 2021
    Abstract

  • A multi-scale real-time tracking algorithm for unmanned aerial vehicle (UAV) based on dual-attention shuffling is proposed to solve the problems of small size, large scale variation and similar object interference which often occur during UAV object tracking. First, considering the small number of target pixels in the UAV view, a deep network with double sampling integration is constructed, which provides semantic information-rich depth features and preserves the target’s detailed information. Next, a dual-attention shuffling module is designed. Channel attention and spatial attention are simultaneously grouped to filter the extracted feature information, and then the information between different channels is shuffled to enhance information exchange and improve the discriminative ability of the algorithm. Finally, to utilize the feature information of different layers, multiple region proposal networks are added to complete the target classification and regression, and the results are weighted and fused for the UAV target characteristics. Results show that the success and precision rates of the algorithm are 60.3% and 79.3% on the dataset, respectively, with 37.5 frame/s. The algorithm discrimination ability and multi-scale adaptation are significantly enhanced, which can effectively deal with the common challenges in UAV tracking.

     

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