基于深度学习的无人机单目标跟踪综述OA

Deep learning-based UAV(unmanned aerial vehicle)single object tracking has emerged as a critical research area in computer vision,aiming to accurately track designated targets in aerial video sequences.UAV tracking presents unique challenges,including drastic perspective changes,variable target scales,and computational constraints.This survey system-atically categorizes recent methods into three technical approaches:traditional Siamese networks,CNN-Transformer hybrid architectures,and full Transformer methods,focusing on advances from 2022 to 2025.This paper proposes innovative sub-classifcation frameworks,including:module replacement,feature post-fusion,and collaborative modeling for CNN-Transformer hybrid architectures;static computation,hybrid mechanisms,and dynamic computation for single-stream Transformer methods.These frameworks reveal the evolution from performance-oriented to efficiency-performance balanced optimization.Comprehensive evaluations on UAV123,DTB70,UAVDT,and VisDrone2018 datasets validate the ad-vantages and limitations of different approaches.This paper identifies key challenges with future directions and engi-neering deployment guidance.