Boosting UAV Tracking with Voxel-Based Trajectory-Aware Pre-Training

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

Research output: Contribution to journalArticlepeer-review


Siamese network-based object tracking has remarkably promoted the automatic capability for highly-maneuvered unmanned aerial vehicles (UAVs). However, the leading-edge tracking framework often depends on template matching, making it trapped when facing multiple views of object in consecutive frames. Moreover, the general image-level pretrained backbone can overfit to holistic representations, causing the misalignment to learn object-level properties in UAV tracking. To tackle these issues, this work presents TRTrack, a comprehensive framework to fully exploit the stereoscopic representation for UAV tracking. Specifically, a novel pre-training paradigm method is proposed. Through trajectory-aware reconstruction training (TRT), the capability of the backbone to extract stereoscopic structure feature is strengthened without any parameter increment. Accordingly, an innovative hierarchical self-attention Transformer is proposed to capture the local detail information and global structure knowledge. For optimizing the correlation map, we proposed a novel spatial correlation refinement (SCR) module, which promotes the capability of modeling the long-range spatial dependencies. Comprehensive experiments on three UAV challenging benchmarks demonstrate that the proposed TRTrack achieves superior UAV tracking performance in both precision and efficiency. Quantitative tests in real-world settings fully prove the effectiveness of our work.

Original languageEnglish (US)
Pages (from-to)1133-1140
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Feb 1 2023


  • Unmanned aerial vehicle
  • hierarchical self-attention Transformer
  • self-supervised learn- ing
  • visual object tracking
  • voxel-based trajectory-aware pre-training

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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