Fire detection using surveillance systems
<p class="Abstract">In this research, I present a video-based system to detect Fire in real time taking advantage of already existing surveillance systems for Fire detection either inside or outside the building, Detection of fires with surveillance cameras is characterized by early...
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| Language: | English |
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Academy Publishing Center
2024-02-01
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| Series: | Advances in Computing and Engineering |
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| Online Access: | http://apc.aast.edu/ojs/index.php/ACE/article/view/774 |
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| _version_ | 1839635113743220736 |
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| author | Hanan Samir Mahmoud |
| author_facet | Hanan Samir Mahmoud |
| author_sort | Hanan Samir Mahmoud |
| collection | DOAJ |
| description | <p class="Abstract">In this research, I present a video-based system to detect Fire in real time taking advantage of already existing surveillance systems for Fire detection either inside or outside the building, Detection of fires with surveillance cameras is characterized by early detection and rapid performance. Information about the progress of the fire can be obtained through live video. Also vision-based is capable of providing forensic evidence. The basic idea of the research is Fire detection based on video; I proposed Fourier descriptors to describe reddish moving objects. The proposed system idea is to detect reddish moving bodies in every frame and correlate the detections with the same reddish bodiest over time. Multi-threshold segmentation is used to divide the image. This method can be integrated with pretreatment and post-processing. The threshold is one of the most common ways to divide the image. The next stage after the segmentation is to obtain the reddish body features. The feature is created by obtaining the contour of the reddish body and estimating the normalized Fourier descriptors of it. If the reddish body contour's Fourier descriptors vary from frame to frame then we can predict the fire.</p><p class="Abstract"><strong>Received: 18 December 2023 </strong></p><p class="Abstract"><strong>Accepted: 06 February 2024 </strong></p><p class="Abstract"><strong>Published: 23 February 2024</strong></p> |
| format | Article |
| id | doaj-art-19aa3fda3d3544e28e64ad42761f8b45 |
| institution | Matheson Library |
| issn | 2735-5977 2735-5985 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Academy Publishing Center |
| record_format | Article |
| series | Advances in Computing and Engineering |
| spelling | doaj-art-19aa3fda3d3544e28e64ad42761f8b452025-07-09T11:34:13ZengAcademy Publishing CenterAdvances in Computing and Engineering2735-59772735-59852024-02-0141445110.21622/ACE.2024.04.1.774335Fire detection using surveillance systemsHanan Samir Mahmoud0Researcher at Housing and Building National Research Center<p class="Abstract">In this research, I present a video-based system to detect Fire in real time taking advantage of already existing surveillance systems for Fire detection either inside or outside the building, Detection of fires with surveillance cameras is characterized by early detection and rapid performance. Information about the progress of the fire can be obtained through live video. Also vision-based is capable of providing forensic evidence. The basic idea of the research is Fire detection based on video; I proposed Fourier descriptors to describe reddish moving objects. The proposed system idea is to detect reddish moving bodies in every frame and correlate the detections with the same reddish bodiest over time. Multi-threshold segmentation is used to divide the image. This method can be integrated with pretreatment and post-processing. The threshold is one of the most common ways to divide the image. The next stage after the segmentation is to obtain the reddish body features. The feature is created by obtaining the contour of the reddish body and estimating the normalized Fourier descriptors of it. If the reddish body contour's Fourier descriptors vary from frame to frame then we can predict the fire.</p><p class="Abstract"><strong>Received: 18 December 2023 </strong></p><p class="Abstract"><strong>Accepted: 06 February 2024 </strong></p><p class="Abstract"><strong>Published: 23 February 2024</strong></p>http://apc.aast.edu/ojs/index.php/ACE/article/view/774tracking moving objects, object segmentation, fourier descriptors |
| spellingShingle | Hanan Samir Mahmoud Fire detection using surveillance systems Advances in Computing and Engineering tracking moving objects, object segmentation, fourier descriptors |
| title | Fire detection using surveillance systems |
| title_full | Fire detection using surveillance systems |
| title_fullStr | Fire detection using surveillance systems |
| title_full_unstemmed | Fire detection using surveillance systems |
| title_short | Fire detection using surveillance systems |
| title_sort | fire detection using surveillance systems |
| topic | tracking moving objects, object segmentation, fourier descriptors |
| url | http://apc.aast.edu/ojs/index.php/ACE/article/view/774 |
| work_keys_str_mv | AT hanansamirmahmoud firedetectionusingsurveillancesystems |