UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking

TL;DR

UAUTrack presents a unified multimodal framework for Anti-UAV visual tracking, effectively integrating multiple data modalities with a novel text prompt strategy, achieving state-of-the-art results and balancing accuracy with efficiency.

Contribution

The paper introduces UAUTrack, a novel end-to-end single-stream model that unifies multimodal Anti-UAV tracking and incorporates a text prompt strategy for improved focus.

Findings

Achieves state-of-the-art performance on Anti-UAV datasets.

Balances accuracy and speed effectively across scenarios.

Demonstrates the effectiveness of multimodal data fusion.

Abstract

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.