Room Occupancy Estimator For a Communal Workplace

This project explores the use of machine learning and computer vision to estimate room occupancy in communal workplaces. By applying the YOLO object detection framework with Roboflow, the system detects and counts individuals in real time from image inputs. The model achieved promising accuracy and demonstrates potential applications in energy management, such as adaptive lighting and HVAC control. The study highlights how AI-driven occupancy estimation can contribute to energy efficiency, sustainability, and smart city development. It provides a foundation for integrating intelligent control systems with renewable energy adoption in modern buildings.