Grant: Alliance Grants

Measurement of building occupant information is important for energy efficiency, comfort, health, productivity, and security management. Given the current global pandemic, Distech Controls Inc. seeks to automatically detect building occupants with symptoms of COVID-19, or in close proximity to one another, and thereby limit propagation of the COVID-19 virus. The main objective of this project is to develop compact privacy-preserving deep learning (DL) models for building occupancy measurement systems that allow detecting people with high fevers, and in close proximity to others, and thereby limit the propagation of COVID-19. These models will rely on low-resolution multimodal (RGB and thermal IR) cameras that are co-located on a wall, portal, or ceiling of a building to estimate the physical distance, density, and temperature of people in a room. Expanding on its building occupancy measurement systems, Distech seeks to develop cost-effective DL models for cross-modal person detection, counting, and re-identification. This project involves a cross-disciplinary team from ETS and Distech, and will allow to intensify the exchange of ideas and resources, and establish long-term collaborative links. Focusing on the design of DL models for visual recognition applications from low, this project will focus on state-of-the-art research. By focusing on the development of cost-effective deep learning (DL) models for COVID detection through the multi-modal fusion of lower-resolution RGB-IR sensors, we anticipate that this project will lead to innovative AI technologies. Significant findings of this research project will be disseminated in high caliber scientific journals and conferences, and integrated into Distech building management solutions. This project also offers the opportunity for the training of highly qualified personnel to face current and future challenges in areas of strategic interest.