Enhancing Class Monitoring Through Video Analytics and Deep Learning Models

This research paper introduces an innovative solution for enhancing class monitoring by integrating video analytics and advanced deep-learning models for human activity recognition. Through a comparative analysis of two implementation methods, Deep Neural Network (DNN) models emerge as the more efficient and scalable choice, revolutionizing the approach to class monitoring in educational and security contexts. The study evaluates four key deep-learning models, including ConvLSTM, LRCN, and CNN+GRU, highlighting their superior performance over traditional feature-based approaches. Notably, the accuracy metrics for these models are as follows: ConvLSTM achieved an accuracy of 0.62, LRCN exhibited exceptional accuracy at 0.94, and GRU + Convolution Model demonstrated robust accuracy of 0.92. The ultimate goal is the development of an automated monitoring system leveraging deep learning to detect and classify human activities, with significant implications for education and security. These DNN models, known for their cost-effectiveness and adaptability, can be swiftly deployed to monitor classrooms and examination rooms, promptly identifying and reporting suspicious activity. Rigorous performance evaluation using diverse metrics underlines the strengths and weaknesses of these models, marking a significant advancement in automated monitoring systems’ capabilities.