Research Article
Real-Time Traffic Monitoring and Status Detection with a Multi-vehicle Tracking System
@INPROCEEDINGS{10.1007/978-3-030-97603-3_2, author={Lu Wang and Chan Tong Lam and K. L. Eddie Law and Benjamin Ng and Wei Ke and Marcus Im}, title={Real-Time Traffic Monitoring and Status Detection with a Multi-vehicle Tracking System}, proceedings={Intelligent Transport Systems. 5th EAI International Conference, INTSYS 2021, Virtual Event, November 24-26, 2021, Proceedings}, proceedings_a={INTSYS}, year={2022}, month={3}, keywords={Traffic transport systems Multi-vehicle tracking Road traffic status Deep learning algorithms}, doi={10.1007/978-3-030-97603-3_2} }- Lu Wang
Chan Tong Lam
K. L. Eddie Law
Benjamin Ng
Wei Ke
Marcus Im
Year: 2022
Real-Time Traffic Monitoring and Status Detection with a Multi-vehicle Tracking System
INTSYS
Springer
DOI: 10.1007/978-3-030-97603-3_2
Abstract
With live street videos posted online, the Macao Government provides means to the general public to assess the latest road traffic conditions. After reviewing over these videos, a person may decide to change the travel route from the one he or she initially plans to take. To let road users make decisions better and faster, it would be desirable to design an automated software, being a component of an Intelligent Transport System, which offers proper suggestions to the users instantly upon analyzing all available live videos. In this paper, we propose to create a real-time road traffic condition estimation system. Its design is based on a combination of deep learning algorithms: the YOLOv5, DeepSORT, and the Non-Maximum Suppression algorithms. Putting together the YOLOv5 with our proposed two-stage NMS strategy, the improvement on the efficiency of object detection on live videos is noticeable. Our two-stage strategy removes the requirement to manually tune the NMS parameters continuously. With DeepSORT, we are able to track moving vehicles, and create motion trajectories, which we can use filtering strategy to assess the latest road traffic conditions. Since different lanes on a road may have different traffic situations, we separate the lanes based on angles and propose to use a lane status score independently for each lane. Through the experimental results, our system design could estimate the traffic status in real-time without requiring any manual parametric adjustments.
