An AI-Based Smart CCTV System for Classroom Energy Monitoring and Management

Abstract

Electricity wastage in the educational institutions is a problem that is often ignored, wherein ceiling fans are often not switched off even after classrooms are vacated. Manual monitoring of such situations is not efficient, and can be a challenge to keep up over a large campus, leading to unnecessary energy consumption. Although a number of occupancy detection systems based on computer vision have been proposed, most of them only deal with the recognition of the human presence; there is little dealing with the verification of the true operating state of electrical appliances. To overcome this drawback, this paper introduces an AI-based smart classroom monitoring mechanism based on the existing CVC system infrastructure, capable of detecting the human presence and ceiling fan operation and report them, in real time. The proposed system is constructed according to a modified YOLOv8 deep learning system with a custom classroom dataset for a two-object detection. In addition, a simple motion analysis technique is used within the region detected fan to identify if the fan is running or stationary. By combining the detection of people with the verification of the state of the appliances, the system added a context-aware decision mechanism that generates alerts only when a fan is found to be operating in an empty classroom. Such an approach helps in reducing false alarms while also focused on the direct root cause of wastage of electricity. The proposed solution is cost-effective, scalable and can be deployed in smart classrooms and academic campuses to aid efficient energy management.