Exploring the Integration of Deep Learning in CCTV Systems for Enhanced Security Measures in Academic Libraries

Abstract

Aim: The study investigates how deep learning, particularly YOLOv4, might be included into the CCTV systems of the Federal Polytechnic Ile-Oluji Library for proactive monitoring, real-time anomaly detection, and resource optimization. Selected for real-time surveillance in dynamic environments, YOLOv4 balances speed and accuracy against alternatives like R-CNN. Sample: Examining CCTV footage from the Federal Polytechnic Ile-Oluji Library assisted one to identify prospective security issues, heavy usage hours, and activity trends. Study Design: A study leveraging YOLOv4's capacity for activity tracking, anomaly identification, and efficient use of library grounds resources. Conducted in the Federal Polytechnic Ile-Oluji Library, eight months of research spanning January 2024 through October 2024. Methodology: YOLOv4's deep learning architecture was integrated with library CCTV footage to enable real-time video analysis. The evaluation of its performance was conducted using the F1 score, recall, and precision metrics. As privacy and environmental adaptation emerged as focal points, heatmaps revealed areas of significant activity. R-CNN and similar methodologies were not utilized due to their slower processing speeds and higher computational demands, which limit their applicability in real-time settings. Results: The system attained accuracy, recall, and F1 ratings above 90%. The Peak library activity was noted between 10 AM and 2 PM, which helped to guide resources. YOLOv4 improved operating efficiency and library security by detecting questionable activity rather well. Conclusion: YOLOv4 has been demonstrated to be a powerful instrument for real-time surveillance, surpassing the efficiency and practicality of conventional methods and other deep learning models such as R-CNN in this application. This work shows how deep learning could turn CCTV systems into intelligent monitoring systems, therefore opening the path for safer academic surroundings.