Raspberry-based Low-resolution Thermal image system using a Smoothing Filter-based Kalman

Abstract

Since the emergence of global epidemics such as SARS-CoV-2, H1N1, SARS and MERS, a wide range of systems for measuring temperature have been developed based on computer vision to reduce and prevent the virus contagious. By implementing a Raspberry-based Low-resolution embedded system based and a FLIR Lepton® sensor human body temperature is measured and improved by four different algorithms implemented. Firstly, three traditional time-series processes solving such as, Simple Mean (SM), Simple Moving Average (SMA), and Multi Lineal Regression (MLR), and secondly, and online filter-based Kalman predictor were implemented to increase the signal to noise ratio of the acquired temperature magnitude. Results of average prediction for different benchmarks demonstrate the best performance of Kalman Filter upon traditional processes. In addition, this algorithm achieves to smooth output temperature with fewer samples (∼10% of total samples) in comparison MLR and SMA. Finally, Raspberry-based Low-resolution Thermal image system is a feasible tool as a high-speed temperature estimator, by implementation of algorithms codified in Python language.