Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications

Lelio Campanile, Fiammetta Marulli, Michele Mastroianni, Gianfranco Palmiero, Carlo Sanghez

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.

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Paper Citation


in Harvard Style

Campanile L., Marulli F., Mastroianni M., Palmiero G. and Sanghez C. (2021). Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications. In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoTs, ISBN 978-989-758-504-3, pages 343-353. DOI: 10.5220/0010537803430353


in Bibtex Style

@conference{ai4eiots21,
author={Lelio Campanile and Fiammetta Marulli and Michele Mastroianni and Gianfranco Palmiero and Carlo Sanghez},
title={Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications},
booktitle={Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoTs,},
year={2021},
pages={343-353},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010537803430353},
isbn={978-989-758-504-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoTs,
TI - Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications
SN - 978-989-758-504-3
AU - Campanile L.
AU - Marulli F.
AU - Mastroianni M.
AU - Palmiero G.
AU - Sanghez C.
PY - 2021
SP - 343
EP - 353
DO - 10.5220/0010537803430353