UAV monitoring and forecasting model in intelligent traffic oriented applications

Abstract

Intelligent transportation system is a traffic management system developed with the progress of society and traffic. Its idea is to integrate the real-time operation of people, vehicles, roads and traffic involved in the traffic. The purpose of this paper is to build a safe, reliable and efficient vehicle monitoring and forecasting model for IOT. Based on the Beidou satellite positioning technology and Lora communication technology, aiming at the problem that the deep learning detection method cannot meet the real-time requirements in processing the monitoring video, this paper proposes a method of using multiple single target trackers instead of some yolov3 detection tasks, and puts forward the design idea and specific implementation scheme of the vehicle monitoring and prediction model. The vehicle monitoring and prediction model is used to detect four kinds of targets, namely, small cars, buses, trucks and pedestrians. The multi-target trajectory tracking is used to carry out the traffic statistics of multi vehicle types, the detection of two kinds of abnormal behaviors of traffic targets is low speed and parking, and the capture of pedestrians. The experimental results show that the vehicle monitoring and prediction model has the highest accuracy of location and type recognition for four types of traffic objects, namely, small cars, trucks, buses and pedestrians, reaching 80%.