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

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@article{IJASEIT10195,
   author = {Ruhana Abang Yusup and Wang Hui Hui and Wee Bui Lin},
   title = {A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {2},
   year = {2021},
   pages = {531--541},
   keywords = {Intelligent traffic surveillance; semantical analysis; traffic images; traffic density.},
   abstract = {

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.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10195},    doi = {10.18517/ijaseit.11.2.10195} }

EndNote

%A Abang Yusup, Ruhana
%A Hui, Wang Hui
%A Lin, Wee Bui
%D 2021
%T A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
%B 2021
%9 Intelligent traffic surveillance; semantical analysis; traffic images; traffic density.
%! A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
%K Intelligent traffic surveillance; semantical analysis; traffic images; traffic density.
%X 

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.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10195 %R doi:10.18517/ijaseit.11.2.10195 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 2 %@ 2088-5334

IEEE

Ruhana Abang Yusup,Wang Hui Hui and Wee Bui Lin,"A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, pp. 531-541, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.2.10195.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Abang Yusup, Ruhana
AU  - Hui, Wang Hui
AU  - Lin, Wee Bui
PY  - 2021
TI  - A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 2
Y2  - 2021
SP  - 531
EP  - 541
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Intelligent traffic surveillance; semantical analysis; traffic images; traffic density.
N2  - 

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.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10195 DO - 10.18517/ijaseit.11.2.10195

RefWorks

RT Journal Article
ID 10195
A1 Abang Yusup, Ruhana
A1 Hui, Wang Hui
A1 Lin, Wee Bui
T1 A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 2
YR 2021
SP 531
OP 541
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Intelligent traffic surveillance; semantical analysis; traffic images; traffic density.
AB 

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.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10195 DO - 10.18517/ijaseit.11.2.10195