基于频域特征和Transformer的无人机目标跟踪算法OA

With the rapid development of Unmanned Aerial Vehicle(UAV)technology,target tracking has become one of the key techniques in UAV applications.To address challenges such as occlusion,deformation,scale varia-tion,and multi-view changes in UAV target tracking,this paper proposes a UAV target tracking algorithm based on frequency-domain feature and Transformer architecture.First,a distilled Transformer network is employed to extract global spatial features from images,and an adaptive frequency-domain deep network is employed to capture detailed frequency-domain features.meanwhile,a learning image is introduced at the input stage to capture the correlation be-tween the target template and the search region,thereby updating the initial target template and enhancing target rep-resentation.Second,a multi-view invariant feature learning strategy based on mutual information maximization is pro-posed.By maximizing the mutual information between the target template and the search template,a novel loss func-tion is designed to improve the network's robustness against target appearance variations.Finally,the target position is determined according to the feature responses of the learning image.Simulation results demonstrate that the pro-posed algorithm effectively improves UAV target tracking accuracy and exhibits strong robustness under complex scenarios.