Abstract: ‍ ‍Aiming at the problems of tracking failure caused by poor detection performance of small targets， large target scale changes， and complex background interference under the unmanned aerial vehicle platform， this paper proposed an unmanned aerial vehicle multi-target tracking algorithm that jointly optimized YOLOv5 （You Only Look Once） and Deep-SORT （Simple Online and Realtime Tracking with a Deep Association Metric）. The algorithm used the improved CSPDarknet53 （Cross Stage Paritial Darknet53） backbone network to reconstruct the feature extraction module in the detector. At the same time， the small target detection layer was designed by the top-down and bottom-up bidirectional fusion network. In the meanwhile， the optimized target detection network model was trained by the unmanned aerial vehicle aerial photography dataset， which solved the problem of poor detection performance of small targets. As for the tracking module， a residual network combined with the spatiotemporal attention module was proposed as a feature extraction network to enhance the network's ability to perceive small appearance features and anti-interference. Finally， the triple loss function was used to strengthen the ability of the neural network to distinguish within-class differences. The experimental results show that the average detection accuracy of the optimized target detection is improved by 11% compared with the original YOLOv5， and the accuracy and precision of the UAVDT（The Unmanned Aerial Vehicle Benchmark： Object Detection and Tracking） data set are improved by 13.288% and 3.968% respectively compared with the original tracking algorithm， effectively reducing the target. Identity switching frequency.