An Infrared Thermography Model Enabling Remote Body Temperature Screening Up to 10 Meters

Jing Wei Chin, Kwan Long Wong, Tsz Tai Chan, Kristian Suhartono, Richard H.Y. So; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 3875-3881

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.