AI BASED CCTV ANALYSIS FOR STUDENT ENTRY/EXIT MONITORING

Abstract

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 de-tection and tracking fast, scalable, and highly efficient, even in complex environments with high population movement.

This survey paper reviews recent research developments, approaches, and implementation methods that apply AI-based CCTV analysis to student entry–exit scenarios. 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 op-erational benefits, the survey identifies key challenges such as oc-clusion, 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.