Real-Time Vehicle Detection and Urban Traffic Behavior Analysis Based on
  UAV Traffic Videos on Mobile Devices
Yuan Zhu Yanqiang Wang Yadong An Hong Yang Yiming Pan
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.