Aerial object tracking algorithm for UAVs based on dual-attention shuffling

A multi-scale real-time tracking algorithm for unmanned aerial vehicle (UAV) based on dual-attention shuffling is proposed to solve the problems of small size, large scale variation and similar object interference which often occur during UAV object tracking. First, considering the small number of target pixels in the UAV view, a deep network with double sampling integration is constructed, which provides semantic information-rich depth features and preserves the target’s detailed information. Next, a dual-attention shuffling module is designed. Channel attention and spatial attention are simultaneously grouped to filter the extracted feature information, and then the information between different channels is shuffled to enhance information exchange and improve the discriminative ability of the algorithm. Finally, to utilize the feature information of different layers, multiple region proposal networks are added to complete the target classification and regression, and the results are weighted and fused for the UAV target characteristics. Results show that the success and precision rates of the algorithm are 60.3% and 79.3% on the dataset, respectively, with 37.5 frame/s. The algorithm discrimination ability and multi-scale adaptation are significantly enhanced, which can effectively deal with the common challenges in UAV tracking. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.