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Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection
Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently bee...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098535/ https://www.ncbi.nlm.nih.gov/pubmed/37050721 http://dx.doi.org/10.3390/s23073661 |
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author | Li, Shenglin Yoon, Hwan-Sik |
author_facet | Li, Shenglin Yoon, Hwan-Sik |
author_sort | Li, Shenglin |
collection | PubMed |
description | Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle’s bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections. |
format | Online Article Text |
id | pubmed-10098535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100985352023-04-14 Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection Li, Shenglin Yoon, Hwan-Sik Sensors (Basel) Article Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle’s bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections. MDPI 2023-03-31 /pmc/articles/PMC10098535/ /pubmed/37050721 http://dx.doi.org/10.3390/s23073661 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Shenglin Yoon, Hwan-Sik Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection |
title | Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection |
title_full | Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection |
title_fullStr | Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection |
title_full_unstemmed | Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection |
title_short | Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection |
title_sort | vehicle localization in 3d world coordinates using single camera at traffic intersection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098535/ https://www.ncbi.nlm.nih.gov/pubmed/37050721 http://dx.doi.org/10.3390/s23073661 |
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