Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning

He, Yutong; Zhang, Hui; Arens, Edward; Merritt, Alexander; Huizenga, Charlie; Levinson, Ronnen; Wang, Andy; Ghahramani, Ali; Alvarez-Suarez, Ana

doi:10.1016/j.buildenv.2022.109811

Cited by 20 publications

(3 citation statements)

References 37 publications

(72 reference statements)

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“…The main advantage of CIOPS-RGBD is that it can provide reliable occupancy 3D position and furniture information under complex and densely populated scenarios, so that it can be used to identify the actual application scenes in the room. This information can be very helpful from different aspects:(1) It can help to evaluate the cooling and heating load in the room to adjust the HVAC system’s operating parameters and strategy.(2) It can improve the control of lights according to occupancy location, to save energy for the lighting system.(3) Occupancy position can provide target information for other sensors to capture more refined images or thermal images for the analysis of the occupants’ thermal sensation, 75 which can further improve the indoor environment.(4) The real application scenarios of the room can help the room function area division and facility design, to achieve a better living environment(5) The information provided by the method has the potential for the analysis of the thermal behaviour of people in the room.…”

Section: Resultsmentioning

confidence: 99%

“…(3) Occupancy position can provide target information for other sensors to capture more refined images or thermal images for the analysis of the occupants’ thermal sensation, 75 which can further improve the indoor environment.…”

Section: Resultsmentioning

confidence: 99%

“…(2) It can improve the control of lights according to occupancy location, to save energy for the lighting system. (3) Occupancy position can provide target information for other sensors to capture more refined images or thermal images for the analysis of the occupants' thermal sensation, 75 which can further improve the indoor environment. (4) The real application scenarios of the room can help the room function area division and facility design, to achieve a better living environment (5) The information provided by the method has the potential for the analysis of the thermal behaviour of people in the room.…”

Section: Potential Application Of Ciops-rgbdmentioning

confidence: 99%

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An RGB-D camera-based indoor occupancy positioning system for complex and densely populated scenarios

Wang

2023

Indoor and Built Environment

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The dynamic changes in occupants’ presence, movements and other behaviours could affect the actual operation and energy consumption of buildings. The design and operation of buildings can be adjusted to be more energy efficient by thinking over the actual distribution of occupants and application scenarios. Nevertheless, accurate, nonintrusive and applicable occupancy positioning systems, especially for complex and densely populated scenarios, have not yet been established. Herein, we propose a novel indoor positioning system based on a red green blue-depth (RGB-D) camera (CIOPS-RGBD). This system utilizes multiple RGB-D cameras to capture colour and depth images from different views. A data fusion algorithm is developed according to the result from a human pose estimation method. Then, the proposed CIOPS-RGBD system was setup and verified within a multifunction room for steady and dynamic accuracy estimation. The accuracy was verified within 10 cm in most cases. Finally, the system was tested under different application scenarios with more than 25 occupants in an 86 m2 space. The results demonstrate that the system can provide high-quality occupancy positioning and body orientation information for these scenarios in almost real-time, providing a solid basis to improve the actual operation and design of indoor environment creation systems.

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