{"ID":2831617,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.07385","arxiv_id":"2512.07385","title":"How Far are Modern Trackers from UAV-Anti-UAV? A Million-Scale Benchmark and New Baseline","abstract":"Unmanned Aerial Vehicles (UAVs) offer wide-ranging applications but also pose significant safety and privacy violation risks in areas like airport and infrastructure inspection, spurring the rapid development of Anti-UAV technologies in recent years. However, current Anti-UAV research primarily focuses on RGB, infrared (IR), or RGB-IR videos captured by fixed ground cameras, with little attention to tracking target UAVs from another moving UAV platform. To fill this gap, we propose a new multi-modal visual tracking task termed UAV-Anti-UAV, which involves a pursuer UAV tracking a target adversarial UAV in the video stream. Compared to existing Anti-UAV tasks, UAV-Anti-UAV is more challenging due to severe dual-dynamic disturbances caused by the rapid motion of both the capturing platform and the target. To advance research in this domain, we construct a million-scale dataset consisting of 1,810 videos, each manually annotated with bounding boxes, a language prompt, and 15 tracking attributes. Furthermore, we propose MambaSTS, a Mamba-based baseline method for UAV-Anti-UAV tracking, which enables integrated spatial-temporal-semantic learning. Specifically, we employ Mamba and Transformer models to learn global semantic and spatial features, respectively, and leverage the state space model's strength in long-sequence modeling to establish video-level long-term context via a temporal token propagation mechanism. We conduct experiments on the UAV-Anti-UAV dataset to validate the effectiveness of our method. A thorough experimental evaluation of 50 modern deep tracking algorithms demonstrates that there is still significant room for improvement in the UAV-Anti-UAV domain. The dataset and codes will be available at {\\color{magenta}https://github.com/983632847/Awesome-Multimodal-Object-Tracking}.","short_abstract":"Unmanned Aerial Vehicles (UAVs) offer wide-ranging applications but also pose significant safety and privacy violation risks in areas like airport and infrastructure inspection, spurring the rapid development of Anti-UAV technologies in recent years. However, current Anti-UAV research primarily focuses on RGB, infrared...","url_abs":"https://arxiv.org/abs/2512.07385","url_pdf":"https://arxiv.org/pdf/2512.07385v1","authors":"[\"Chunhui Zhang\",\"Li Liu\",\"Zhipeng Zhang\",\"Yong Wang\",\"Hao Wen\",\"Xi Zhou\",\"Shiming Ge\",\"Yanfeng Wang\"]","published":"2025-12-08T10:19:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":606144,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2831617,"paper_url":"https://arxiv.org/abs/2512.07385","paper_title":"How Far are Modern Trackers from UAV-Anti-UAV? A Million-Scale Benchmark and New Baseline","repo_url":"https://github.com/983632847/Awesome-Multimodal-Object-Tracking","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}