基于检测和重识别的无人机行人跟踪算法

Combining intelligent detection and tracking algorithms with the flexibility of unmanned aerial vehicle (UAV) is a hot research topic for UAV applications. A UAV pedestrian tracking algorithm based on detection and re-identification was proposed for solving the problems of target slippage and occlusion due to the UAV’s viewpoint and motion. Firstly, TensorRT acceleration of trained YOLOv5 was performed to solve the problem of limited UAV computational resources; secondly, a pedestrian tracking algorithm framework was constructed based on a target detection algorithm and a re-identification algorithm with quantization acceleration; finally, the pedestrian matching degree was designed and determined to complete the pedestrian matching system design. Simulation experiments show that the trained YOLOv5 and OSNet have certain accuracy, and the YOLOv5 network with TensorRT acceleration has nearly 50% improvement in frame rate with guaranteed accuracy. The flight test shows that the proposed algorithm can achieve stable tracking of the target under the situation of pedestrian intersection and obstacle occlusion, and it has certain practicality and effectiveness.