Digital Guardians: Exploring Video Analytics in Autonomous Classroom Surveillance Systems
K Sreekar, Gurusamy Jeyakumar · 2025
The growing prevalence of intelligent surveillance systems has expedited progress in video analytics, particularly in learning environments. This survey article analyzes various methods for assessing student behavior and extracting features from classroom videos, with a focus on identifying test malpractices. We look at many existing systems that use techniques like facial identification, tracking, gesture recognition, and engagement analysis to monitor student activities. Here we shed light on the research needs by showing the benefits and limitations of several strategies that involve coping with occlusions, variable lighting, and small face sections in video frames. Our goal is to develop a strong system that uses live video feeds to detect test malpractices in a better and accurate manner while being efficient.