Venue & metadata

• Journal/Proceedings: International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings
• Volume: 2021-April
• Pages: 343 – 353
• Note: Cited by: 0
• Author keywords: Clinical Evaluation; Covid-19 Disease; Deep Learning; Health Monitoring; Internet of Things; Machine Learning; Mass Screening Infection; Smart Sensor Networks

Abstract

In this paper, we introduce a solution aiming to improve the accuracy of the surface temperature detection in an outdoor environment. The temperature sensing subsystem relies on Mobotix thermal camera without the black body, the automatic compensation subsystem relies on Raspberry Pi with Node-RED and TensorFlow 2.x. The final results showed that it is possible to automatically calibrate the camera using machine learning and that it is possible to use thermal imaging cameras even in critical conditions such as outdoors. Future development is to improve performance using computer vision techniques to rule out irrelevant measurements. © 2021 by SCITEPRESS - Science and Technology Publications, Lda.

Keywords

Cameras GS Deep learning GS Diagnosis GS Diseases GS Infrared devices GS Infrared imaging GS Learning systems GS Sensor networks GS Temperature sensors GS Automatic calibration GS Clinical evaluation GS Covid-19 disease GS Deep learning GS Health monitoring GS Machine-learning GS Mass screening infection GS Mass screenings GS Smart sensor networks GS Thermal camera GS Internet of things GS

Links & artifacts

Publisher Copy BibTeX Download .bib

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Suggested citation

Campanile, L., Marulli, F., Mastroianni, M., Palmiero, G., & Sanghez, C. (2021). Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 343–353. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137959400&partnerID=40&md5=eb78330cb4d585e500b77cd906edfbc7