An Infrared Thermography Model Enabling Remote Body Temperature Screening Up to 10 Meters
Abstract
During the COVID-19 pandemic, temperature screening has emerged as a common practice in the infection control pipeline. In particular, thermal imaging systems have risen in popularity for preliminary screening of individuals with elevated temperatures, especially in high throughput areas. However, remote temperature measurement is intrinsically complex and susceptible to unavoidable influences from the measuring environment. We study the effects of sensor-subject distance on remote temperature readings and present an infrared-based system for rapid temperature screening over long distances (2 m to 10 m). The system applies a state-of-the-art pose estimation algorithm to extract the face box locations, sensor-subject distances, and facial temperatures within a scene. For the use of infrared thermography in humans, we propose a thermal compensation model to correct the temperature of subjects measured at different distances and perform analyses to evaluate the trade-off between missing rate (elevated temperature does not trigger an alarm) and false alarm rate (normal temperature triggers an alarm). The experimental results show our system’s promise to identify subjects with elevated temperatures and the potential to improve temperature screening protocols in different environments.
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A single temperature measurement can indicate whether a patient has fever or hypothermia.
“A single temperature measurement can tell us whether the patient has a fever or hypothermia [150,151,152,153,154].”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [151]
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The Current State of Optical Sensors in Medical Wearables
Vavrinsky, E., Esfahani, N. E., Hausner, M., Kuzma, A., Rezo, V., Donoval, M., & Kosnacova, H. (2022) · Biosensors · doi:10.3390/bios12040217 · Cited by 103 · see citing works
- Authors
- Chin, J. W., Long Wong, K., Chan, T. T., Suhartono, K., & So, R. H. Y.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 3870-3876
- OpenAlex
- W3186857926
- Cited by
- 16
- References
- 22
- Citations by year
- 2025: 6 · 2024: 1 · 2023: 4 · 2022: 4 · 2021: 1
- FWCI
- 3.14
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- University Grants Committee
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Infrared Thermography in Medicine
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Engineering › Mechanics of Materials › Thermography and Photoacoustic Techniques
- Keywords
- infrared thermography, face pose estimation, COVID-19 infection control, false alarm rate, missing rate
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