Adversarial Blur-Deblur Network for Robust UAV Tracking

Haobo Zuo, Changhong Fu, Sihang Li, Kunhan Lu, Yiming Li, Chen Feng

Research output: Contribution to journalArticlepeer-review


Unmanned aerial vehicle (UAV) tracking has been widely applied in real-world applications such as surveillance and monitoring. However, the inherent high maneuverability and agility of UAV often lead to motion blur, which can impair the visual appearance of the target object and easily degrade the existing trackers. To overcome this challenge, this work proposes a tracking-oriented adversarial blur-deblur network (ABDNet), composed of a novel deblurrer to recover the visual appearance of the tracked object, and a brand-new blur generator to produce realistic blurry images for adversarial training. More specifically, the deblurrer progressively refines the features through pixel-wise, spatial-wise, and channel-wise stages to achieve excellent deblurring performance. The blur generator adaptively fuses an image sequence with a learnable kernel to create realistic blurry images. During training, ABDNet is plugged into the state-of-the-art real-time trackers and trained with blurring-deblurring loss as well as tracking loss. During inference, the blur generator is removed, while the deblurrer and the tracker can work together for UAV tracking. Extensive experiments in both public datasets and real-world testing have validated the effectiveness of ABDNet.

Original languageEnglish (US)
Pages (from-to)1101-1108
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Feb 1 2023


  • Unmanned aerial vehicle
  • adversarial training
  • realistic blur generator
  • robust image deblurrer
  • visual object tracking

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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