Gas sensors and real-time video for accurate classroom occupancy detection.

This study introduces an advanced methodology for optimizing HVAC efficiency through real-time classroom occupancy detection by combining video analysis with gas sensor data to enhance accuracy and reliability. The proposed system integrates video feeds captured by a Logitech C20 webcam with data from an MS1100 gas sensor module, ensuring a dual-modal approach to occupancy detection. A YOLOv4 object detection model, trained on a diverse dataset of over 20,000 labeled human face images, achieves over 98 % accuracy in identifying and counting occupants in real time. OpenCV is employed to facilitate efficient and seamless processing of video streams, enabling the system to deliver real-time results crucial for dynamic HVAC control. The integration of gas sensor data addresses scenarios where environmental factors, such as low light or obstructions, could impair video analysis, thereby improving detection reliability under diverse conditions. The combination of these modalities provides a robust and adaptable framework for occupancy detection, which can be scaled for different building types and configurations. This method demonstrates significant potential in reducing energy consumption and enhancing the sustainability of building management systems by providing precise occupancy data for HVAC optimization. The approach offers a practical and scalable solution for the growing demand for energy-efficient infrastructure in smart buildings. The architecture ensures seamless integration between visual and environmental sensing modalities, enhancing real-time responsiveness and occupancy detection reliability.•Utilizes a YOLOv4 object detection model and MS1100 gas sensor for real-time occupancy detection.•Achieves over 98 % accuracy with a dataset of over 20,000 labeled human face images.•Offers a scalable and efficient solution for energy-efficient HVAC systems.