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RESEARCH PRODUCT

Implementing an embedded system to identify possible COVID-19 suspects using thermovision cameras

Valentin FleacaAdrian Florea

subject

Coronavirus disease 2019 (COVID-19)Computer sciencebusiness.industryDeep learningmedia_common.quotation_subjectImage processing03 medical and health sciencesALARMStatistical classification0302 clinical medicine030225 pediatricsFace (geometry)Reading (process)Embedded systemGlobal Positioning System030212 general & internal medicineArtificial intelligencebusinessmedia_common

description

The main goal of this paper is to prove that by combining thermal vision cameras and image processing with many deep learning classification algorithms we developed an effective embedded system with high applicability in this critical period caused by COVID-19 pandemic disease. Using fixed and mobile thermal cameras we envisioned and developed a real time temperature screening capable of sending alarm signals over network or by SMS to local authorities along with multiple detection metrics such as the age, the gender, the facial emotion, the GPS location where the alarm went off, the temperature reading from the human face and also if the subject is wearing or not a medical face mask.

yearjournalcountryeditionlanguage
2020-10-082020 24th International Conference on System Theory, Control and Computing (ICSTCC)
https://doi.org/10.1109/icstcc50638.2020.9259699