Vision‐based vehicle speed estimation: A survey
Abstract The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits are considere...
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2021-08-01
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Series: | IET Intelligent Transport Systems |
Online Access: | https://doi.org/10.1049/itr2.12079 |
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doaj-6f8e899582214a3a9aee542e5e341d1c2021-07-14T13:20:15ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-08-01158987100510.1049/itr2.12079Vision‐based vehicle speed estimation: A surveyDavid Fernández Llorca0Antonio Hernández Martínez1Iván García Daza2Computer Engineering Department University of Alcalá University Campus, Alcalá de Henares Madrid SpainComputer Engineering Department University of Alcalá University Campus, Alcalá de Henares Madrid SpainComputer Engineering Department University of Alcalá University Campus, Alcalá de Henares Madrid SpainAbstract The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits are considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision‐based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision‐based vehicle speed estimation. The terminology and the application domains are described and a complete taxonomy of a large selection of works that categorizes all stages involved is proposed. An overview of performance evaluation metrics and available datasets is provided. Finally, current limitations and future directions are discussed.https://doi.org/10.1049/itr2.12079 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David Fernández Llorca Antonio Hernández Martínez Iván García Daza |
spellingShingle |
David Fernández Llorca Antonio Hernández Martínez Iván García Daza Vision‐based vehicle speed estimation: A survey IET Intelligent Transport Systems |
author_facet |
David Fernández Llorca Antonio Hernández Martínez Iván García Daza |
author_sort |
David Fernández Llorca |
title |
Vision‐based vehicle speed estimation: A survey |
title_short |
Vision‐based vehicle speed estimation: A survey |
title_full |
Vision‐based vehicle speed estimation: A survey |
title_fullStr |
Vision‐based vehicle speed estimation: A survey |
title_full_unstemmed |
Vision‐based vehicle speed estimation: A survey |
title_sort |
vision‐based vehicle speed estimation: a survey |
publisher |
Wiley |
series |
IET Intelligent Transport Systems |
issn |
1751-956X 1751-9578 |
publishDate |
2021-08-01 |
description |
Abstract The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits are considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision‐based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision‐based vehicle speed estimation. The terminology and the application domains are described and a complete taxonomy of a large selection of works that categorizes all stages involved is proposed. An overview of performance evaluation metrics and available datasets is provided. Finally, current limitations and future directions are discussed. |
url |
https://doi.org/10.1049/itr2.12079 |
work_keys_str_mv |
AT davidfernandezllorca visionbasedvehiclespeedestimationasurvey AT antoniohernandezmartinez visionbasedvehiclespeedestimationasurvey AT ivangarciadaza visionbasedvehiclespeedestimationasurvey |
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