Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications
Published in International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021
Recommended citation: Lelio Campanile, Fiammetta Marulli, Michele Mastroianni, Gianfranco Palmiero, Carlo Sanghez, "Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications." International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137959400&partnerID=40&md5=eb78330cb4d585e500b77cd906edfbc7
Cited by: 0
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.
Author Keywords: Clinical Evaluation; Covid-19 Disease; Deep Learning; Health Monitoring; Internet of Things; Machine Learning; Mass Screening Infection; Smart Sensor Networks
Bibtex citation:
@CONFERENCE{Campanile2021343,
author = "Campanile, Lelio and Marulli, Fiammetta and Mastroianni, Michele and Palmiero, Gianfranco and Sanghez, Carlo",
title = "Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications",
year = "2021",
journal = "International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings",
volume = "2021-April",
pages = "343 – 353",
type = "Conference paper"
}