Thermal Vision: Pioneering Non-Invasive Temperature Tracking in Congested Spaces

TL;DR

This paper introduces a non-invasive thermal vision system using thermal cameras and edge devices for accurate temperature monitoring in dense settings, addressing limitations of prior sparse-focused methods.

Contribution

It presents a novel system combining YOLO-based face detection with regression for temperature estimation tailored for dense environments, with publicly available dataset and code.

Findings

Face detection model achieves over 84 mAP in various settings.

Regression framework attains 0.18°C mean square error and 0.96 R^2 score.

System proves effective for real-world dense environment temperature monitoring.

Abstract

Non-invasive temperature monitoring of individuals plays a crucial role in identifying and isolating symptomatic individuals. Temperature monitoring becomes particularly vital in settings characterized by close human proximity, often referred to as dense settings. However, existing research on non-invasive temperature estimation using thermal cameras has predominantly focused on sparse settings. Unfortunately, the risk of disease transmission is significantly higher in dense settings like movie theaters or classrooms. Consequently, there is an urgent need to develop robust temperature estimation methods tailored explicitly for dense settings. Our study proposes a non-invasive temperature estimation system that combines a thermal camera with an edge device. Our system employs YOLO models for face detection and utilizes a regression framework for temperature estimation. We evaluated the system on a diverse dataset collected in dense and sparse settings. Our proposed face detection model achieves an impressive mAP score of over 84 in both in-dataset and cross-dataset evaluations. Furthermore, the regression framework demonstrates remarkable performance with a mean square error of 0.18$^{\circ}$C and an impressive $R^2$ score of 0.96. Our experiments' results highlight the developed system's effectiveness, positioning it as a promising solution for continuous temperature monitoring in real-world applications. With this paper, we release our dataset and programming code publicly.