@article{ref1,
title="Development of AI-based vehicle detection and tracking system for C-ITS application",
journal="Journal of advanced transportation",
year="2021",
author="Tak, Sehyun and Lee, Jong-Deok and Song, Jeongheon and Kim, Sunghoon",
volume="2021",
number="",
pages="e4438861-e4438861",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="0197-6729",
doi="10.1155/2021/4438861",
url="http://dx.doi.org/10.1155/2021/4438861"
}