Integrating Deep Learning and CCTV for Real-Time Health Monitoring in Schools

Abstract—This project proposes a real-time health monitoring system for schools, integrating deep learning techniques with CCTV technology to enhance student well-being. By utilizing advanced computer vision algorithms, the system analyzes video feeds from cameras to continuously assess students' health, focusing on facial expressions and behaviors. The DeepFace model is employed to detect and recognize individual students and analyze their facial expressions for signs of distress or other health-related issues. When the system identifies concerning health indicators, it generates immediate alerts, which are sent to school administrators through a user-friendly web interface. This interface allows administrators to monitor health analytics, view real-time alerts, and access historical data for effective decision-making and intervention. The proposed framework offers a proactive approach to student health monitoring, ensuring a safer and more supportive school environment. Keywords—DeepFace, CCTV, Pattern recognition, colour intensity