This paper focuses on a real-time vehicle detection and urban traffic behavior analysis system based on Unmanned Aerial Vehicle (UAV) traffic video. By using UAV to collect traffic data and combining the YOLOv8 model and SORT tracking algorithm, the object detection and tracking functions are implemented on the iOS mobile platform. For the problem of traffic data acquisition and analysis, the dynamic computing method is used to process the performance in real time and calculate the micro and macro traffic parameters of the vehicles, and real-time traffic behavior analysis is conducted and visualized. The experiment results reveals that the vehicle object detection can reach 98.27% precision rate and 87.93% recall rate, and the real-time processing capacity is stable at 30 frames per seconds. This work integrates drone technology, iOS development, and deep learning techniques to integrate traffic video acquisition, object detection, object tracking, and traffic behavior analysis functions on mobile devices. It provides new possibilities for lightweight traffic information collection and data analysis, and offers innovative solutions to improve the efficiency of analyzing road traffic conditions and addressing transportation issues for transportation authorities.

翻译：本文研究了一种基于无人机交通视频的实时车辆检测与城市交通行为分析系统。通过利用无人机采集交通数据，并结合YOLOv8模型与SORT跟踪算法，在iOS移动平台上实现了目标检测与跟踪功能。针对交通数据采集与分析问题，采用动态计算方法实时处理性能，计算车辆的微观与宏观交通参数，并进行实时交通行为分析与可视化。实验结果表明，车辆目标检测的精确率达到98.27%，召回率为87.93%，实时处理能力稳定在30帧/秒。本工作融合了无人机技术、iOS开发与深度学习技术，在移动设备上集成了交通视频采集、目标检测、目标跟踪与交通行为分析功能。这为轻量级交通信息采集与数据分析提供了新的可能性，并为交通管理部门提高道路通行状况分析效率、解决交通问题提供了创新解决方案。