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A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review

Population growth in large cities has contributed to the increase in vehicles' number, leading to the traffic congestion problem. Incompetent traffic supervision could squander an inconsiderable number of man-hours and might lead to fatal consequences. Therefore, intelligent traffic surveillanc...

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Main Authors: Ruhana, Abang Yusup, Hui Hui, Wang, Bui Lin, Wee
Format: Article
Language:English
Published: IJASEIT 2021
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/36847/1/extraction1.pdf
http://ir.unimas.my/id/eprint/36847/
http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10195
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spelling my.unimas.ir.368472021-12-01T08:37:16Z http://ir.unimas.my/id/eprint/36847/ A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review Ruhana, Abang Yusup Hui Hui, Wang Bui Lin, Wee Q Science (General) T Technology (General) Population growth in large cities has contributed to the increase in vehicles' number, leading to the traffic congestion problem. Incompetent traffic supervision could squander an inconsiderable number of man-hours and might lead to fatal consequences. Therefore, intelligent traffic surveillance systems have to carry more significant roles in highway monitoring and traffic management system throughout the years. Although vehicle detection and classification methods have evolved rapidly throughout the years, they still lack high-level reasoning. Accurate and precise vehicle recognition and classification are still insufficient to develop an intelligent and reliable traffic system. There is a demand to increase the confidence in image understanding and effectively extract the images conformed to human perception and without human interference. This paper attempts to summarize a review on several methods that semantically extract and analyze traffic density with image processing techniques. Three (3) methods that have been selected to be discussed in this paper are semantic analysis of traffic video using image understanding, mining semantic context details of traffic scene, and integrating vision and language in semantic description of traffic events from image sequences. Each method is discussed thoroughly, and their outstanding issue is deliberated in this paper. IJASEIT 2021-04-30 Article PeerReviewed text en http://ir.unimas.my/id/eprint/36847/1/extraction1.pdf Ruhana, Abang Yusup and Hui Hui, Wang and Bui Lin, Wee (2021) A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review. International Journal on Advanced Science, Engineering and Information Technology, 11 (2). pp. 531-541. ISSN 2088-5334 http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10195
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Ruhana, Abang Yusup
Hui Hui, Wang
Bui Lin, Wee
A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
description Population growth in large cities has contributed to the increase in vehicles' number, leading to the traffic congestion problem. Incompetent traffic supervision could squander an inconsiderable number of man-hours and might lead to fatal consequences. Therefore, intelligent traffic surveillance systems have to carry more significant roles in highway monitoring and traffic management system throughout the years. Although vehicle detection and classification methods have evolved rapidly throughout the years, they still lack high-level reasoning. Accurate and precise vehicle recognition and classification are still insufficient to develop an intelligent and reliable traffic system. There is a demand to increase the confidence in image understanding and effectively extract the images conformed to human perception and without human interference. This paper attempts to summarize a review on several methods that semantically extract and analyze traffic density with image processing techniques. Three (3) methods that have been selected to be discussed in this paper are semantic analysis of traffic video using image understanding, mining semantic context details of traffic scene, and integrating vision and language in semantic description of traffic events from image sequences. Each method is discussed thoroughly, and their outstanding issue is deliberated in this paper.
format Article
author Ruhana, Abang Yusup
Hui Hui, Wang
Bui Lin, Wee
author_facet Ruhana, Abang Yusup
Hui Hui, Wang
Bui Lin, Wee
author_sort Ruhana, Abang Yusup
title A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
title_short A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
title_full A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
title_fullStr A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
title_full_unstemmed A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
title_sort semantic extraction and analysis for traffic density using traffic images: a critical review
publisher IJASEIT
publishDate 2021
url http://ir.unimas.my/id/eprint/36847/1/extraction1.pdf
http://ir.unimas.my/id/eprint/36847/
http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10195
_version_ 1718930118175358976
score 13.211869

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