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Research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system.

This study designs and implements a boundary detection and dangerous area warning algorithm based on deep learning from the perspective of typified campus security situation resources such as data, information, and knowledge. Based on integrating multiple campus security factors, real-time perceptio...

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Main Authors: Zhong, Baitong, Mohamad Sharif, Johan, Salam, Sah, Ran, Chengke, Liang, Yizhou, Cheng, Zijun
Format: Article
Language:English
Published: IADITI ? International Association for Digital Transformation and Technological Innovation 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/106758/1/JohanMohamadSharif2023_ResearchOptimizationofBoundaryDetectionandDangerousArea.pdf
http://eprints.utm.my/106758/
http://dx.doi.org/10.55267/iadt.07.13844
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spelling my.utm.1067582024-07-20T01:58:16Z http://eprints.utm.my/106758/ Research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system. Zhong, Baitong Mohamad Sharif, Johan Salam, Sah Ran, Chengke Liang, Yizhou Cheng, Zijun TA Engineering (General). Civil engineering (General) This study designs and implements a boundary detection and dangerous area warning algorithm based on deep learning from the perspective of typified campus security situation resources such as data, information, and knowledge. Based on integrating multiple campus security factors, real-time perception and further prediction of campus security situation can be achieved. Through coordinated operation among various algorithm modules, object intrusion in specific areas can be accurately identified and early warning can be given. The research results show that when an object invades a specific area, the difference coefficient will increase, and the larger the change value in the intrusion area, the larger the corresponding difference coefficient. By using this feature, the threshold of the difference coefficient can be determined. When a region is invaded, the contour length of the foreground will sharply increase. Based on the statistical information of the contour length of the foreground, the threshold can be set to determine whether someone has invaded the region. The deep learning algorithm in this study accurately extracts the contour of moving targets and can identify foreground targets. The real-time performance of the algorithm is also guaranteed, and it has high practical value in intelligent video monitoring. This algorithm greatly improves the efficiency of intrusion detection by utilizing the joint constraints of two types of time-domain and scene-space transformations in monitoring images. This method is not affected by the brightness of the regional environment, nor will it cause misjudgment due to significant differences in brightness of the regional environment. The detection and inference time of deep learning-based detection methods is controlled within 2-3ms, and the FPS value of the detection method is always at a high level, which can quickly increase to over 350frames/s after transmission begins. The detection method based on deep learning has higher detection efficiency. IADITI ? International Association for Digital Transformation and Technological Innovation 2023-10 Article PeerReviewed application/pdf en http://eprints.utm.my/106758/1/JohanMohamadSharif2023_ResearchOptimizationofBoundaryDetectionandDangerousArea.pdf Zhong, Baitong and Mohamad Sharif, Johan and Salam, Sah and Ran, Chengke and Liang, Yizhou and Cheng, Zijun (2023) Research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system. Journal of Information Systems Engineering and Management, 8 (4). pp. 1-11. ISSN 2468-4376 http://dx.doi.org/10.55267/iadt.07.13844 DOI:10.55267/iadt.07.13844
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Zhong, Baitong
Mohamad Sharif, Johan
Salam, Sah
Ran, Chengke
Liang, Yizhou
Cheng, Zijun
Research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system.
description This study designs and implements a boundary detection and dangerous area warning algorithm based on deep learning from the perspective of typified campus security situation resources such as data, information, and knowledge. Based on integrating multiple campus security factors, real-time perception and further prediction of campus security situation can be achieved. Through coordinated operation among various algorithm modules, object intrusion in specific areas can be accurately identified and early warning can be given. The research results show that when an object invades a specific area, the difference coefficient will increase, and the larger the change value in the intrusion area, the larger the corresponding difference coefficient. By using this feature, the threshold of the difference coefficient can be determined. When a region is invaded, the contour length of the foreground will sharply increase. Based on the statistical information of the contour length of the foreground, the threshold can be set to determine whether someone has invaded the region. The deep learning algorithm in this study accurately extracts the contour of moving targets and can identify foreground targets. The real-time performance of the algorithm is also guaranteed, and it has high practical value in intelligent video monitoring. This algorithm greatly improves the efficiency of intrusion detection by utilizing the joint constraints of two types of time-domain and scene-space transformations in monitoring images. This method is not affected by the brightness of the regional environment, nor will it cause misjudgment due to significant differences in brightness of the regional environment. The detection and inference time of deep learning-based detection methods is controlled within 2-3ms, and the FPS value of the detection method is always at a high level, which can quickly increase to over 350frames/s after transmission begins. The detection method based on deep learning has higher detection efficiency.
format Article
author Zhong, Baitong
Mohamad Sharif, Johan
Salam, Sah
Ran, Chengke
Liang, Yizhou
Cheng, Zijun
author_facet Zhong, Baitong
Mohamad Sharif, Johan
Salam, Sah
Ran, Chengke
Liang, Yizhou
Cheng, Zijun
author_sort Zhong, Baitong
title Research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system.
title_short Research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system.
title_full Research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system.
title_fullStr Research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system.
title_full_unstemmed Research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system.
title_sort research on optimization of boundary detection and dangerous area warning algorithms based on deep learning in campus security system.
publisher IADITI ? International Association for Digital Transformation and Technological Innovation
publishDate 2023
url http://eprints.utm.my/106758/1/JohanMohamadSharif2023_ResearchOptimizationofBoundaryDetectionandDangerousArea.pdf
http://eprints.utm.my/106758/
http://dx.doi.org/10.55267/iadt.07.13844
_version_ 1805880863305498624
score 13.211869

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