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UAV target tracking method based on global feature interaction and anchor-frame-free perceptual feature modulation.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Unmanned Aerial Vehicle (UAV) target tracking relies on real-time video analysis.
  • Deep learning methods, particularly Siamese-based, show promise but struggle with speed-accuracy trade-offs.
  • Existing methods face challenges in refining features and handling target deformation.

Purpose of the Study:

  • To enhance the accuracy and efficiency of UAV target tracking.
  • To improve feature representation and reduce computational load for real-time applications.
  • To boost tracking performance for small and deforming targets.

Main Methods:

  • Implemented feature fusion within deep inter-correlation operations.
  • Introduced a global attention mechanism to expand the field of view and refine features.
  • Designed an anchor-free frame-aware feature modulation mechanism for efficient anchor generation and target refinement.

Main Results:

  • The proposed algorithm demonstrates a balance between speed and accuracy on UAV tracking datasets (UAV123@10fps, UAV20L, DTB70).
  • Achieved a real-time processing speed of 30 frames per second on the Jetson Orin Nano platform.
  • Showcased improved tracking performance for small targets and adaptability to target deformation.

Conclusions:

  • The developed method offers a robust solution for real-time UAV target tracking.
  • The integration of feature fusion and attention mechanisms significantly enhances tracking capabilities.
  • The algorithm's efficiency and accuracy are validated through extensive experiments and a physical platform implementation.