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-p...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IAES
2023
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| Online Access: | http://eprints.uthm.edu.my/8796/ http://eprints.uthm.edu.my/8796/1/J15807_0082aff3a6d68eae3f24b79bc11d56f6.pdf |
| _version_ | 1848889500456976384 |
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| author | Mohd Aris, Nor Afiqah Jamaian, Siti Suhana |
| author_facet | Mohd Aris, Nor Afiqah Jamaian, Siti Suhana |
| author_sort | Mohd Aris, Nor Afiqah |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T20:27:10Z |
| format | Article |
| id | uthm-8796 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:27:10Z |
| publishDate | 2023 |
| publisher | IAES |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-87962023-06-12T07:36:17Z http://eprints.uthm.edu.my/8796/ Background subtraction challenges in motion detection using Gaussian mixture model: a survey Mohd Aris, Nor Afiqah Jamaian, Siti Suhana TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (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. IAES 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/8796/1/J15807_0082aff3a6d68eae3f24b79bc11d56f6.pdf Mohd Aris, Nor Afiqah and Jamaian, Siti Suhana (2023) Background subtraction challenges in motion detection using Gaussian mixture model: a survey. International Journal of Artificial Intelligence, 12 (3). pp. 1-12. ISSN 2252-8938 https://doi.org/10.11591/ijai.v12.i3.pp1007-1018 |
| spellingShingle | TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) Mohd Aris, Nor Afiqah Jamaian, Siti Suhana Background subtraction challenges in motion detection using Gaussian mixture model: a survey |
| title | 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_short | 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 |
| topic | TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) |
| url | http://eprints.uthm.edu.my/8796/ http://eprints.uthm.edu.my/8796/ http://eprints.uthm.edu.my/8796/1/J15807_0082aff3a6d68eae3f24b79bc11d56f6.pdf |