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Artificial intelligent systems for vehicle classification: A survey

Digitalization is revolutionizing our way of life and catalyzing the transformation into smart city. Intelligent Transportation System (ITS) being an indispensable component of smart city leverages massive amount of collected information to improve traffic efficiency, thereby creating a safer and co...

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Main Authors:	Tan, Shi Hao, Chuah, Joon Huang, Chow, Chee-Onn, Kanesan, Jeevan, Leong, Hung Yang
Format:	Article
Published:	Engineering Applications of Artificial Intelligence 2024
Subjects:	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/44268/ https://doi.org/10.1016/j.engappai.2023.107497
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id	my.um.eprints.44268
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spelling	my.um.eprints.442682024-07-03T06:41:05Z http://eprints.um.edu.my/44268/ Artificial intelligent systems for vehicle classification: A survey Tan, Shi Hao Chuah, Joon Huang Chow, Chee-Onn Kanesan, Jeevan Leong, Hung Yang QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Digitalization is revolutionizing our way of life and catalyzing the transformation into smart city. Intelligent Transportation System (ITS) being an indispensable component of smart city leverages massive amount of collected information to improve traffic efficiency, thereby creating a safer and comfortable commuting environment for the users. One of the most important tasks in ITS is vehicle classification which aims to find out the vehicle identity, including vehicle segment, automobile maker, model, etc. In this article, we first present the vehicle classification taxonomy branched based on the nature of input data. We subsequently investigate diverse area of sensor-based vehicle classification followed by image-based vehicle classification which cover both the conventional and emerging techniques in a comprehensive manner. The methodologies together with the corresponding strengths and potential weaknesses are elucidated so that it serves as an invaluable reference for vehicle classification related applications in the future. More importantly, we express our views on future research direction with the intention to accelerate the development of vehicle classification field.In contrast to previous works, we aim to cover wide spectrum of vehicle classification methodologies in this review to provide more clarity when it comes to selecting a solution that suits individual need. They include both sensor-based and image-based vehicle classification for VTR, VLR and VMMR. We employ exhaustive coverage approach for the former to identify the extant solutions that are built upon various kinds of sensing technologies and they are further collated according to the installation methods. For image-based methods, we screen for the works that have been central to both pre-and post-deep learning era. The featured works either address the shortcoming of previous works in image domain or present novel concept to advance the classification performance. Engineering Applications of Artificial Intelligence 2024-03 Article PeerReviewed Tan, Shi Hao and Chuah, Joon Huang and Chow, Chee-Onn and Kanesan, Jeevan and Leong, Hung Yang (2024) Artificial intelligent systems for vehicle classification: A survey. Engineering Applications of Artificial Intelligence, 129. ISSN 0952-1976, DOI https://doi.org/10.1016/j.engappai.2023.107497 <https://doi.org/10.1016/j.engappai.2023.107497>. https://doi.org/10.1016/j.engappai.2023.107497 10.1016/j.engappai.2023.107497
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Tan, Shi Hao Chuah, Joon Huang Chow, Chee-Onn Kanesan, Jeevan Leong, Hung Yang Artificial intelligent systems for vehicle classification: A survey
description	Digitalization is revolutionizing our way of life and catalyzing the transformation into smart city. Intelligent Transportation System (ITS) being an indispensable component of smart city leverages massive amount of collected information to improve traffic efficiency, thereby creating a safer and comfortable commuting environment for the users. One of the most important tasks in ITS is vehicle classification which aims to find out the vehicle identity, including vehicle segment, automobile maker, model, etc. In this article, we first present the vehicle classification taxonomy branched based on the nature of input data. We subsequently investigate diverse area of sensor-based vehicle classification followed by image-based vehicle classification which cover both the conventional and emerging techniques in a comprehensive manner. The methodologies together with the corresponding strengths and potential weaknesses are elucidated so that it serves as an invaluable reference for vehicle classification related applications in the future. More importantly, we express our views on future research direction with the intention to accelerate the development of vehicle classification field.In contrast to previous works, we aim to cover wide spectrum of vehicle classification methodologies in this review to provide more clarity when it comes to selecting a solution that suits individual need. They include both sensor-based and image-based vehicle classification for VTR, VLR and VMMR. We employ exhaustive coverage approach for the former to identify the extant solutions that are built upon various kinds of sensing technologies and they are further collated according to the installation methods. For image-based methods, we screen for the works that have been central to both pre-and post-deep learning era. The featured works either address the shortcoming of previous works in image domain or present novel concept to advance the classification performance.
format	Article
author	Tan, Shi Hao Chuah, Joon Huang Chow, Chee-Onn Kanesan, Jeevan Leong, Hung Yang
author_facet	Tan, Shi Hao Chuah, Joon Huang Chow, Chee-Onn Kanesan, Jeevan Leong, Hung Yang
author_sort	Tan, Shi Hao
title	Artificial intelligent systems for vehicle classification: A survey
title_short	Artificial intelligent systems for vehicle classification: A survey
title_full	Artificial intelligent systems for vehicle classification: A survey
title_fullStr	Artificial intelligent systems for vehicle classification: A survey
title_full_unstemmed	Artificial intelligent systems for vehicle classification: A survey
title_sort	artificial intelligent systems for vehicle classification: a survey
publisher	Engineering Applications of Artificial Intelligence
publishDate	2024
url	http://eprints.um.edu.my/44268/ https://doi.org/10.1016/j.engappai.2023.107497
_version_	1805881148451061760
score	13.211869

Artificial intelligent systems for vehicle classification: A survey

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