Utilizing Automatic Number Plate Recognition for an Intelligent Campus Gate Security System

The Central Luzon State University (CLSU) campus, covering 658 hectares, experiences thousands of vehicle entries and exits daily. Ensuring campus safety and security is of utmost importance, and efficient monitoring of vehicle entry and exit at campus gates is vital. Traditional manual methods of v...

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Bibliographic Details
Published in:	2024 IEEE 6th Symposium on Computers & Informatics (ISCI) pp. 212 - 217
Main Authors:	Limon, John Christopher, Agustus Botangen, Khavee, Malaca, Marc Joel, Bermoza, Ryan, Vidania, Noli
Format:	Conference Proceeding
Language:	English
Published:	IEEE 10-08-2024
Subjects:	Accuracy ANPR Cameras campus gate security image processing Logic gates Manuals real-time identification Real-time systems Safety smart security Traffic control vehicle traffic monitoring
Online Access:	Get full text
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Summary:	The Central Luzon State University (CLSU) campus, covering 658 hectares, experiences thousands of vehicle entries and exits daily. Ensuring campus safety and security is of utmost importance, and efficient monitoring of vehicle entry and exit at campus gates is vital. Traditional manual methods of vehicle identification and recording at the campus gates are often time-consuming, error-prone, and inefficient. This paper presents the design and development of an Automatic Number Plate Recognition (ANPR) system for vehicle monitoring at CLSU. The system includes two main components: an ANPR module that captures real-time number plate images using optical character recognition (OCR) and advanced image processing, and a web-based application for camera management and data handling. The application supports data management, alerts for unrecognized vehicles, analytics, and report generation. Experimental results showed high accuracy, with a success rate of approximately 92 percent for both 4-wheeled and 2-wheeled vehicles, when excluding vehicles outside the camera's range. This system enhances campus gate security by providing real-time and accurate vehicle identification, reducing manual errors, and enabling efficient monitoring of vehicle movements. It facilitates comprehensive data collection which can be analyzed to identify traffic patterns, optimize parking occupancy, and inform overall campus vehicle management and security strategies. Furthermore, the system potentially allows for seamless integration with existing security systems such as CCTV cameras and alarm systems, enhancing the overall security infrastructure of the university community.
ISSN:	2996-6752
DOI:	10.1109/ISCI62787.2024.10667725