Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening

Since the SARS outbreak in 2003, the use of thermal imaging technology for fever mass screening in public areas becomes important. Mass screening is the first stage screening before individual screening for further verification and it is very effective to avoid congestion. In no...

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Main Authors:	Siti Sofiah, Mohd Radzi, Kamarul Hawari, Ghazali, Nazriyah, Che Zan, Faradila, Naim
Format:	Conference or Workshop Item
Language:	English
Published:	2011
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/2112/ http://umpir.ump.edu.my/id/eprint/2112/ http://umpir.ump.edu.my/id/eprint/2112/1/ProcediaS%2BBS_Template_WCIT-2011_belum_upload.pdf

id	ump-2112
recordtype	eprints
spelling	ump-21122018-02-21T06:48:26Z http://umpir.ump.edu.my/id/eprint/2112/ Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening Siti Sofiah, Mohd Radzi Kamarul Hawari, Ghazali Nazriyah, Che Zan Faradila, Naim TA Engineering (General). Civil engineering (General) Since the SARS outbreak in 2003, the use of thermal imaging technology for fever mass screening in public areas becomes important. Mass screening is the first stage screening before individual screening for further verification and it is very effective to avoid congestion. In non-contact temperature measurement, the medial canthal - between the corner of the eyes and lachrymal (tear) duct - is the best area to represent the elevated body temperature for fever detection. The available thermal technology only provides the ability to detect temperature dimension of objects instead of the regions of interest. For instance, if there is similarity in temperature between regions of background and medial canthal area, thermal camera alone is unable to identify the correct region. Hitherto, in most of installed thermal imaging in airports, this problem is only solved by human operators, thus its effectiveness is influenced by human factors. In this paper, an algorithm based on Gaussian Bi -modal Mixture Models (GBMM) is proposed for background-foreground segmentation as an important feature to identify medial canthal area. To estimate the bi - modal background-foreground distribution mixture parameters, Expectation-Maximization (EM) algorithm is applied and the images are clustered statistically and linearly. The results are later multiplied with background subtraction results to get a better quality of segmentation. The 640x480 thermal resolution imagery sequences are used as input and the intensity of the pixels are collected from arriving passengers in Kuala Lumpur International Airport (KLIA) under a controlled ambient temperature. Twenty image sequences were used in the experiments and the result shown the feasibility of the proposed algorithm. 2011 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/2112/1/ProcediaS%2BBS_Template_WCIT-2011_belum_upload.pdf Siti Sofiah, Mohd Radzi and Kamarul Hawari, Ghazali and Nazriyah, Che Zan and Faradila, Naim (2011) Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening. In: 2nd World Conference on Information Technology, 24-28 November 2011 , Antalya, Turkey. . http://www.elsevier.com/locate/procedia
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Siti Sofiah, Mohd Radzi Kamarul Hawari, Ghazali Nazriyah, Che Zan Faradila, Naim Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening
description	Since the SARS outbreak in 2003, the use of thermal imaging technology for fever mass screening in public areas becomes important. Mass screening is the first stage screening before individual screening for further verification and it is very effective to avoid congestion. In non-contact temperature measurement, the medial canthal - between the corner of the eyes and lachrymal (tear) duct - is the best area to represent the elevated body temperature for fever detection. The available thermal technology only provides the ability to detect temperature dimension of objects instead of the regions of interest. For instance, if there is similarity in temperature between regions of background and medial canthal area, thermal camera alone is unable to identify the correct region. Hitherto, in most of installed thermal imaging in airports, this problem is only solved by human operators, thus its effectiveness is influenced by human factors. In this paper, an algorithm based on Gaussian Bi -modal Mixture Models (GBMM) is proposed for background-foreground segmentation as an important feature to identify medial canthal area. To estimate the bi - modal background-foreground distribution mixture parameters, Expectation-Maximization (EM) algorithm is applied and the images are clustered statistically and linearly. The results are later multiplied with background subtraction results to get a better quality of segmentation. The 640x480 thermal resolution imagery sequences are used as input and the intensity of the pixels are collected from arriving passengers in Kuala Lumpur International Airport (KLIA) under a controlled ambient temperature. Twenty image sequences were used in the experiments and the result shown the feasibility of the proposed algorithm.
format	Conference or Workshop Item
author	Siti Sofiah, Mohd Radzi Kamarul Hawari, Ghazali Nazriyah, Che Zan Faradila, Naim
author_facet	Siti Sofiah, Mohd Radzi Kamarul Hawari, Ghazali Nazriyah, Che Zan Faradila, Naim
author_sort	Siti Sofiah, Mohd Radzi
title	Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening
title_short	Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening
title_full	Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening
title_fullStr	Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening
title_full_unstemmed	Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening
title_sort	using bimodal gaussian mixture model-based algorithm for background segmentation in thermal fever mass screening
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/2112/ http://umpir.ump.edu.my/id/eprint/2112/ http://umpir.ump.edu.my/id/eprint/2112/1/ProcediaS%2BBS_Template_WCIT-2011_belum_upload.pdf
first_indexed	2023-09-18T21:55:40Z
last_indexed	2023-09-18T21:55:40Z
_version_	1777414062392475648

Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening

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