TEAM:
Prof. Dusan Licina, Tenure Track Assistant Professor (HOBEL), Prof. Alexandre Alahi (VITA)
Human indoor activities are directly linked to levels of various indoor air pollutants and thermal comfort experience. Therefore, accurate assessment of occupancy metrics (e.g., occupancy number and their activities) is important for better control of indoor environments. It is evident that sustainable buildings of the future will increasingly rely on sensors to control heating, ventilation and air-conditioning (HVAC) systems to manage indoor air quality and thermal comfort. However, there is limited information available on means to cost- effectively characterize occupant metrics in all types of buildings. With advancements in Artificial Intelligence, more precisely Computer Vision, an opportunity has been opened up for better control of HVAC systems. This project aims to investigate a novel vision-based human activity detector in order to improve the control algorithm of HVAC system. Better HVAC control is ultimately important for improved human comfort, health and well- being.