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: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IADITI ? International Association for Digital Transformation and Technological Innovation
2023
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| Subjects: | |
| 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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| Summary: | 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. |
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