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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.02668 |
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| _version_ | 1866918228205240320 |
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| author | Ren, Qionglin Zhang, Dawei Tian, Chunxu Zhang, Dan |
| author_facet | Ren, Qionglin Zhang, Dawei Tian, Chunxu Zhang, Dan |
| contents | Research in Anti-UAV (Unmanned Aerial Vehicle) tracking has explored various modalities, including RGB, TIR, and RGB-T fusion. However, a unified framework for cross-modal collaboration is still lacking. Existing approaches have primarily focused on independent models for individual tasks, often overlooking the potential for cross-modal information sharing. Furthermore, Anti-UAV tracking techniques are still in their infancy, with current solutions struggling to achieve effective multimodal data fusion. To address these challenges, we propose UAUTrack, a unified single-target tracking framework built upon a single-stream, single-stage, end-to-end architecture that effectively integrates multiple modalities. UAUTrack introduces a key component: a text prior prompt strategy that directs the model to focus on UAVs across various scenarios. Experimental results show that UAUTrack achieves state-of-the-art performance on the Anti-UAV and DUT Anti-UAV datasets, and maintains a favourable trade-off between accuracy and speed on the Anti-UAV410 dataset, demonstrating both high accuracy and practical efficiency across diverse Anti-UAV scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02668 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking Ren, Qionglin Zhang, Dawei Tian, Chunxu Zhang, Dan Computer Vision and Pattern Recognition Research in Anti-UAV (Unmanned Aerial Vehicle) tracking has explored various modalities, including RGB, TIR, and RGB-T fusion. However, a unified framework for cross-modal collaboration is still lacking. Existing approaches have primarily focused on independent models for individual tasks, often overlooking the potential for cross-modal information sharing. Furthermore, Anti-UAV tracking techniques are still in their infancy, with current solutions struggling to achieve effective multimodal data fusion. To address these challenges, we propose UAUTrack, a unified single-target tracking framework built upon a single-stream, single-stage, end-to-end architecture that effectively integrates multiple modalities. UAUTrack introduces a key component: a text prior prompt strategy that directs the model to focus on UAVs across various scenarios. Experimental results show that UAUTrack achieves state-of-the-art performance on the Anti-UAV and DUT Anti-UAV datasets, and maintains a favourable trade-off between accuracy and speed on the Anti-UAV410 dataset, demonstrating both high accuracy and practical efficiency across diverse Anti-UAV scenarios. |
| title | UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.02668 |