A domain generalized UAV tracking framework via frequency-aware learning and target-aligned data augmentation in complex environments

Abstract

Unmanned Aerial Vehicle (UAV) tracking plays a critical role in airborne autonomous systems, supporting applications such as disaster response, agricultural monitoring, and military surveillance. However, existing tracking methods often exhibit poor generalization in real-world deployments due to domain shifts between the training and target environments. We propose DGTrack, a novel single-source domain generalization framework for UAV visual tracking. DGTrack integrates a Frequency-Aware Learning (FAL) module that separates and adaptively modulates low- and high-frequency components to reduce stylistic interference while enhancing content representation. In addition, a Target-Aligned Augmentation (TAA) module is introduced to improve source domain diversity through multi-level transformations and to align predictions between original and augmented frames by maximizing mutual information. Extensive experiments on the UAVDT and VisDrone2019 datasets demonstrate that DGTrack achieves superior generalization to unseen domains and consistently outperforms state-of-the-art UAV trackers in single-source settings.