统一多模态反无人机跟踪框架，提升精度与效率
UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking
- 单流单阶段端到端架构融合多模态数据
- 在Anti-UAV和DUT Anti-UAV上达最优性能
- 文本提示引导模型专注识别无人机场景
反无人机（Anti-UAV）跟踪研究已探索多种模态，包括RGB、TIR及RGB-T融合。然而，跨模态协作的统一框架仍不完善。现有方法多采用独立模型处理单一任务，忽视跨模态信息共享潜力。此外，反无人机跟踪技术尚处初期，现有方案难以实现有效多模态融合。为此，本文提出UAUTrack，一种基于单流、单阶段、端到端架构的统一单目标跟踪框架，可高效整合多模态信息。该框架引入关键组件：文本先验提示策略，引导模型在多样化场景中聚焦无人机。实验结果表明，UAUTrack在Anti-UAV和DUT Anti-UAV数据集上达到当前最优性能，并在Anti-UAV410数据集上保持精度与速度的良好平衡，展现出在多样反无人机场景中的高精度与实用效率。
原文摘要 · Abstract (English)
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.
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