AI-based CCTV Analysis for Student Entry/Exit Monitoring
Keywords:
AI-based CCTV, Automated attendance, Deep learning, Real-time tracking, Smart surveillance, Student entry–exit monitoring, YOLO object detectionAbstract
Artificial intelligence (AI) has significantly reshaped the role of surveillance systems by enabling them to function beyond simple video recording. In educational institutions, where the safety and tracking of student movement are crucial, the traditional practices of manual attendance or RFID-based access control often fall short in accuracy, speed, and misuse prevention. AI-based CCTV analysis provides an opportunity to automate entry and exit monitoring without requiring physical interaction from students or continuous manual supervision. Deep learning- based models such as YOLO have made real-time person detection and tracking fast, scalable, and highly efficient, even in complex environments with high population movement. This study presents an analysis and evaluation of an AI-based CCTV monitoring system for student entry and exit tracking, along with a review of related research. It highlights the technological backbone including object detection algorithms, Python-based processing, and SQL database integration while critically examining their performance, feasibility, and limitations in real-world institutional settings. In addition to studying the operational benefits, the survey identifies key challenges such as occlusion, lighting variations, privacy concerns, and computational resource demands. The overall objective of this paper is to offer an in-depth understanding of how AI-powered surveillance can improve campus security, automate attendance documentation, and reduce management workload, while outlining opportunities for future enhancement.
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