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About this paper
Xinyu Zhou, Jinglun Li, Lingyi Hong, Kaixun Jiang, Pinxue Guo, Weifeng Ge, Wenqiang Zhang
DeTrack presents an "in-model latent denoising learning" paradigm for visual object tracking that embeds multi-step denoising within a single network forward pass. This approach achieves real-time inference at 30-42 FPS and shows competitive performance, outperforming state-of-the-art methods like ARTrack on GOT-10k by 2.4% AUC and SeqTrack on AVisT by 2.4% AUC.
Key insights
- The multi-step denoising process can be effectively embedded within a single forward pass of a transformer network, achieving both efficiency and the robustness benefits of denoising learning.
- Learning to denoise noisy bounding boxes improves the tracker's generalization capability, making it more resilient to variations and unseen target conditions.
- A compound memory system, combining dynamic visual template updates and historical trajectory information, is essential for robust long-term tracking and re-acquisition in challenging environments.
Summary by alphaXiv.
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