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Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach
Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and ba...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386704/ https://www.ncbi.nlm.nih.gov/pubmed/22778605 http://dx.doi.org/10.3390/s120505623 |
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author | Zulkifley, Mohd Asyraf Moran, Bill Rawlinson, David |
author_facet | Zulkifley, Mohd Asyraf Moran, Bill Rawlinson, David |
author_sort | Zulkifley, Mohd Asyraf |
collection | PubMed |
description | Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good in filtering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications. |
format | Online Article Text |
id | pubmed-3386704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-33867042012-07-09 Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach Zulkifley, Mohd Asyraf Moran, Bill Rawlinson, David Sensors (Basel) Article Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good in filtering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications. Molecular Diversity Preservation International (MDPI) 2012-05-02 /pmc/articles/PMC3386704/ /pubmed/22778605 http://dx.doi.org/10.3390/s120505623 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Zulkifley, Mohd Asyraf Moran, Bill Rawlinson, David Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach |
title | Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach |
title_full | Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach |
title_fullStr | Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach |
title_full_unstemmed | Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach |
title_short | Robust Foreground Detection: A Fusion of Masked Grey World, Probabilistic Gradient Information and Extended Conditional Random Field Approach |
title_sort | robust foreground detection: a fusion of masked grey world, probabilistic gradient information and extended conditional random field approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386704/ https://www.ncbi.nlm.nih.gov/pubmed/22778605 http://dx.doi.org/10.3390/s120505623 |
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