Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation

Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) mul...

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Detalles Bibliográficos
Autores principales: Huang, Xiaohui, He, Pan, Rangarajan, Anand, Ranka, Sanjay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9032018/
https://www.ncbi.nlm.nih.gov/pubmed/35448228
http://dx.doi.org/10.3390/jimaging8040101
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author Huang, Xiaohui
He, Pan
Rangarajan, Anand
Ranka, Sanjay
author_facet Huang, Xiaohui
He, Pan
Rangarajan, Anand
Ranka, Sanjay
author_sort Huang, Xiaohui
collection PubMed
description Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method.
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spelling pubmed-90320182022-04-23 Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation Huang, Xiaohui He, Pan Rangarajan, Anand Ranka, Sanjay J Imaging Article Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method. MDPI 2022-04-06 /pmc/articles/PMC9032018/ /pubmed/35448228 http://dx.doi.org/10.3390/jimaging8040101 Text en © 2022 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
Huang, Xiaohui
He, Pan
Rangarajan, Anand
Ranka, Sanjay
Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation
title Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation
title_full Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation
title_fullStr Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation
title_full_unstemmed Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation
title_short Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation
title_sort machine-learning-based real-time multi-camera vehicle tracking and travel-time estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9032018/
https://www.ncbi.nlm.nih.gov/pubmed/35448228
http://dx.doi.org/10.3390/jimaging8040101
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AT rangarajananand machinelearningbasedrealtimemulticameravehicletrackingandtraveltimeestimation
AT rankasanjay machinelearningbasedrealtimemulticameravehicletrackingandtraveltimeestimation

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