Detection of COVID-19 in Intelligent Building Occupancy Management

智能建筑占用管理中的 COVID-19 检测

• 批准号: 555212-2020
• 负责人: Granger, Eric
• 金额: $ 3.64万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=705765
• 关键词: Detection; COVID; 19; Intelligent; Building

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.

建筑乘员信息的测量对于能源效率，舒适性，健康，生产力和安全管理非常重要。 鉴于当前的全球大流行，Distech Controls Inc.试图自动检测出患有COVID-19症状的建筑物居民，或者彼此近距离接近，从而限制了Covid-19-19病毒的传播。该项目的主要目的是开发具有紧凑的隐私深度学习（DL）模型，用于建筑占用测量系统，这些系统允许检测高发烧的人，并靠近他人，从而限制了Covid-19的传播。这些模型将依靠在建筑物的墙壁，门户或天花板上共处的低分辨率多模式（RGB和热IR）摄像机来估计房间中人员的物理距离，密度和温度。为了扩展其建筑物占用测量系统，Distech试图开发具有成本效益的DL模型，以用于跨模式的人检测，计数和重新识别。该项目涉及来自ET和Distech的跨学科团队，并将允许加强思想和资源的交换，并建立长期的协作链接。该项目着重于低落的视觉识别应用程序的设计，该项目将重点放在最新的研究上。通过关注通过低分辨率RGB-IR传感器的多模式融合来开发具有成本效益的深度学习模型（DL）模型，我们预计该项目将导致创新的AI技术。该研究项目的重大发现将在高素质的科学期刊和会议中传播，并将其整合到分散的建筑管理解决方案中。该项目还为培训高素质的人员提供了机会，以面对战略兴趣领域的当前和未来挑战。