Spectral-Aware Proposal Enhancement for Diffusion-Based Multispectral UAV Multi-Object Tracking

Key points

• Although diffusion-based MOT provides an effective iterative proposal refinement paradigm, existing diffusion-based trackers lack dedicated designs for exploiting multispectral cues in proposal initialization and confidence calibration, limiting tracking robustness in complex UAV scenarios.1
• This letter proposes DSATrack, a diffusion-guided spectral-aware tracking framework for multispectral UAV MOT.1
• Specifically, a lightweight spectral encoder injects multispectral information into the RPN branch through feature fusion, enabling the RPN head to generate high-quality object-aware proposals.1
• Furthermore, a spectral-saliency confidence modulation strategy recalibrates refined tracking confidence according to local multispectral saliency contrast before score thresholding, enhancing spectrally reliable candidates while suppressing unreliable ones.1

Sources (1)

• [1]Spectral-Aware Proposal Enhancement for Diffusion-Based Multispectral UAV Multi-Object Tracking
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 11:36 AM

  Multispectral UAV multi-object tracking (MOT) with oriented bounding boxes remains challenging due to small targets, cluttered backgrounds, and unstable inter-frame associations.

  Although diffusion-based MOT provides an effective iterative proposal refinement paradigm, existing diffusion-based trackers lack dedicated designs for exploiting multispectral cues in proposal initialization and confidence calibration, limiting tracking robustness in complex UAV scenarios.

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