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Background subtraction challenges in motion detection using Gaussian mixture model: a survey

Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-...

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Main Authors: Mohd Aris, Nor Afiqah, Jamaian, Siti Suhana
Format: Article
Language:English
Published: 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/9784/1/J15807_0082aff3a6d68eae3f24b79bc11d56f6.pdf
http://eprints.uthm.edu.my/9784/
https://doi.org/ 10.11591/ijai.v12.i3.pp1007-1018
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spelling my.uthm.eprints.97842023-09-13T07:21:32Z http://eprints.uthm.edu.my/9784/ Background subtraction challenges in motion detection using Gaussian mixture model: a survey Mohd Aris, Nor Afiqah Jamaian, Siti Suhana T Technology (General) Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation. 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9784/1/J15807_0082aff3a6d68eae3f24b79bc11d56f6.pdf Mohd Aris, Nor Afiqah and Jamaian, Siti Suhana (2023) Background subtraction challenges in motion detection using Gaussian mixture model: a survey. IAES International Journal of Artificial Intelligence, 12 (3). pp. 1007-1018. ISSN 2252-8938 https://doi.org/ 10.11591/ijai.v12.i3.pp1007-1018
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Mohd Aris, Nor Afiqah
Jamaian, Siti Suhana
Background subtraction challenges in motion detection using Gaussian mixture model: a survey
description Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation.
format Article
author Mohd Aris, Nor Afiqah
Jamaian, Siti Suhana
author_facet Mohd Aris, Nor Afiqah
Jamaian, Siti Suhana
author_sort Mohd Aris, Nor Afiqah
title Background subtraction challenges in motion detection using Gaussian mixture model: a survey
title_short Background subtraction challenges in motion detection using Gaussian mixture model: a survey
title_full Background subtraction challenges in motion detection using Gaussian mixture model: a survey
title_fullStr Background subtraction challenges in motion detection using Gaussian mixture model: a survey
title_full_unstemmed Background subtraction challenges in motion detection using Gaussian mixture model: a survey
title_sort background subtraction challenges in motion detection using gaussian mixture model: a survey
publishDate 2023
url http://eprints.uthm.edu.my/9784/1/J15807_0082aff3a6d68eae3f24b79bc11d56f6.pdf
http://eprints.uthm.edu.my/9784/
https://doi.org/ 10.11591/ijai.v12.i3.pp1007-1018
_version_ 1778164194920628224
score 13.211869

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