A block-based background model for moving object detection
Detecting the moving objects in a video sequence using a stationary camera is an important task for many computer vision applications. This paper proposes a background subtraction approach. As first step, the background is initialized using the block-based analysis before being updated in each incom...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Computer Vision Center Press
2017-01-01
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| Series: | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| Subjects: | |
| Online Access: | https://elcvia.cvc.uab.es/article/view/855 |
| _version_ | 1818716135718125568 |
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| author | Omar Elharrouss Abdelghafour Abbad Driss Moujahid Jamal Riffi Hamid Tairi |
| author_facet | Omar Elharrouss Abdelghafour Abbad Driss Moujahid Jamal Riffi Hamid Tairi |
| author_sort | Omar Elharrouss |
| collection | DOAJ |
| description | Detecting the moving objects in a video sequence using a stationary camera is an important task for many computer vision applications. This paper proposes a background subtraction approach. As first step, the background is initialized using the block-based analysis before being updated in each incoming frame. Our background frame is generated by collecting the blocks background candidates. The block candidate selection is based on probability density function (pdf) computation. After that, the absolute difference between the background frame and each frame of sequence is computed. A noise filter is applied using the Structure/Texture decomposition in order to minimize the noise caused by background subtraction operation. The binary motion mask is formed using an adaptive threshold that was deduced from the weighted mean and variance calculation. To assure the correspondence between the current frame and the background frame, an adaptation of background model in each incoming frame is realized. After comparing results obtained from the proposed method to other existing ones, it was shown that our approach attains a higher degree of efficacy |
| first_indexed | 2024-12-17T19:14:27Z |
| format | Article |
| id | doaj.art-67653b9ee8e744129dec4bdd42d539cd |
| institution | Directory Open Access Journal |
| issn | 1577-5097 |
| language | English |
| last_indexed | 2024-12-17T19:14:27Z |
| publishDate | 2017-01-01 |
| publisher | Computer Vision Center Press |
| record_format | Article |
| series | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| spelling | doaj.art-67653b9ee8e744129dec4bdd42d539cd2022-12-21T21:35:47ZengComputer Vision Center PressELCVIA Electronic Letters on Computer Vision and Image Analysis1577-50972017-01-0115310.5565/rev/elcvia.855307A block-based background model for moving object detectionOmar Elharrouss0Abdelghafour Abbad1Driss Moujahid2Jamal Riffi3Hamid Tairi4LIIAN Laboratory, Department of Informatics Faculty of Sciences Dhar-Mahraz University of Sidi Mohamed Ben Abdellah Fez, MoroccoLIIAN Laboratory, Department of Informatics Faculty of Sciences Dhar-Mahraz University of Sidi Mohamed Ben Abdellah Fez, MoroccoLIIAN Laboratory, Department of Informatics Faculty of Sciences Dhar-Mahraz University of Sidi Mohamed Ben Abdellah Fez, MoroccoLIIAN Laboratory, Department of Informatics Faculty of Sciences Dhar-Mahraz University of Sidi Mohamed Ben Abdellah Fez, MoroccoLIIAN Laboratory, Department of Informatics Faculty of Sciences Dhar-Mahraz University of Sidi Mohamed Ben Abdellah Fez, MoroccoDetecting the moving objects in a video sequence using a stationary camera is an important task for many computer vision applications. This paper proposes a background subtraction approach. As first step, the background is initialized using the block-based analysis before being updated in each incoming frame. Our background frame is generated by collecting the blocks background candidates. The block candidate selection is based on probability density function (pdf) computation. After that, the absolute difference between the background frame and each frame of sequence is computed. A noise filter is applied using the Structure/Texture decomposition in order to minimize the noise caused by background subtraction operation. The binary motion mask is formed using an adaptive threshold that was deduced from the weighted mean and variance calculation. To assure the correspondence between the current frame and the background frame, an adaptation of background model in each incoming frame is realized. After comparing results obtained from the proposed method to other existing ones, it was shown that our approach attains a higher degree of efficacyhttps://elcvia.cvc.uab.es/article/view/855Motion detectionBackground subtractionBackground modelBackground updateVideo surveillance. |
| spellingShingle | Omar Elharrouss Abdelghafour Abbad Driss Moujahid Jamal Riffi Hamid Tairi A block-based background model for moving object detection ELCVIA Electronic Letters on Computer Vision and Image Analysis Motion detection Background subtraction Background model Background update Video surveillance. |
| title | A block-based background model for moving object detection |
| title_full | A block-based background model for moving object detection |
| title_fullStr | A block-based background model for moving object detection |
| title_full_unstemmed | A block-based background model for moving object detection |
| title_short | A block-based background model for moving object detection |
| title_sort | block based background model for moving object detection |
| topic | Motion detection Background subtraction Background model Background update Video surveillance. |
| url | https://elcvia.cvc.uab.es/article/view/855 |
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