Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

• Published in: Journal of advanced transportation Vol. 2021; pp. 1 - 15
• Main Authors: Tak, Sehyun, Lee, Jong-Deok, Song, Jeongheon, Kim, Sunghoon
• Format: Journal Article
• Language: English
• Published: London Hindawi 19.08.2021; Hindawi Limited; Hindawi-Wiley
• Subjects: Accuracy; Artificial intelligence; Cameras; Classification; Communication; Equipment and supplies; Feasibility studies; Global positioning systems; GPS; High definition; Image classification; Image processing; Intersections; Machine learning; Market penetration; Methods; Monitoring; Monitoring systems; Neural networks; Queues; Radar; Roads; Roads & highways; Sensors; Tracking systems; Traffic; Traffic congestion; Traffic information; Traffic volume; Trajectory analysis; Transportation; Travel; Vehicles
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2021/4438861

There are various means of monitoring traffic situations on roads. Due to the rise of artificial intelligence (AI) based image processing technology, there is a growing interest in developing traffic monitoring systems using camera vision data. This study provides a method for deriving traffic information using a camera installed at an intersection to improve the monitoring system for roads. The method uses a deep-learning-based approach (YOLOv4) for image processing for vehicle detection and vehicle type classification. Lane-by-lane vehicle trajectories are estimated by matching the detected vehicle locations with the high-definition map (HD map). Based on the estimated vehicle trajectories, the traffic volumes of each lane-by-lane traveling direction and queue lengths of each lane are estimated. The performance of the proposed method was tested with thousands of samples according to five different evaluation criteria: vehicle detection rate, vehicle type classification, trajectory prediction, traffic volume estimation, and queue length estimation. The results show a 99% vehicle detection performance with less than 20% errors in classifying vehicle types and estimating the lane-by-lane travel volume, which is reasonable. Hence, the method proposed in this study shows the feasibility of collecting detailed traffic information using a camera installed at an intersection. The approach of combining AI and HD map techniques is the main contribution of this study, which shows a high chance of improving current traffic monitoring systems.