UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking
This addresses the challenge of effective multimodal data fusion for Anti-UAV tracking, which is an incremental improvement in a domain-specific area.
The paper tackled the problem of lacking a unified framework for cross-modal collaboration in Anti-UAV visual tracking by proposing UAUTrack, which integrates multiple modalities and achieves state-of-the-art performance on datasets like Anti-UAV and DUT Anti-UAV.
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.