DFA-MOT: A Dynamic Field-Aware Multi-Object Tracking Framework for Unmanned Aerial Vehicles

Tracking multiple objects in videos captured by unmanned aerial vehicles is challenging due to sudden viewpoint changes, non-linear motion trajectories, and rapid variations in target size and appearance. Existing methods often struggle to handle these complexities, as they rely heavily on handcrafted geometric constraints and fail to adapt to significant field-of-view changes. To address these issues, this paper presents the Dynamic Field-Aware Multi-Object Tracker (DFA-MOT), a joint detection and tracking framework that integrates detection and motion prediction into a unified model, enhancing tracking performance in dynamic UAV environments. The proposed Dynamic Field-of-View Consistency Learning (DFCL) module mitigates geometric distortions caused by UAV movement by leveraging optical flow and learnable deformation operations to achieve progressive spatial alignment. A Scale-Aware Tracking (SAT) mechanism is explored, which enables to accurately predict of both position and scale variations, enhancing the model’s adaptability to variations in target size. By combining detection with predictive motion modeling, DFA-MOT effectively overcomes the limitations of traditional manual constraints. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate that DFA-MOT significantly outperforms state-of-the-art tracking methods in UAV scenarios.