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.

翻译：反无人机跟踪研究已探索了多种模态，包括RGB、TIR以及RGB-T融合。然而，目前仍缺乏一个跨模态协同的统一框架。现有方法主要针对独立任务构建独立模型，往往忽视了跨模态信息共享的潜力。此外，反无人机跟踪技术尚处于起步阶段，现有解决方案难以实现有效的多模态数据融合。为应对这些挑战，我们提出了UAUTrack，一个基于单流、单阶段、端到端架构的统一单目标跟踪框架，能有效整合多种模态。UAUTrack引入了一个关键组件：文本先验提示策略，该策略引导模型聚焦于不同场景下的无人机。实验结果表明，UAUTrack在Anti-UAV和DUT Anti-UAV数据集上取得了最先进的性能，并在Anti-UAV410数据集上保持了精度与速度的良好平衡，展现了其在多样化反无人机场景中的高精度和实用效率。