An integrated and real-time social distancing, mask detection, and facial temperature video measurement system for pandemic monitoring

Abstract

This paper presents a new Edge-AI algorithm for real-time and multi-feature (social distancing, mask detection, and facial temperature) measurement to minimize the spread of COVID-19 among individuals. COVID-19 has extenuated the need for an intelligent surveillance video system that can monitor the status of social distancing, mask detection, and measure the temperature of faces simultaneously using deep learning (DL) models. In this research, we utilized the fusion of three different YOLOv4-tiny object detectors for each task of the integrated system. This DL model is used for object detection and targeted for real-time applications. The proposed models have been trained for different data sets, which include people detection, mask detection, and facial detection for measuring the temperature, and evaluated on these existing data sets. Thermal and visible cameras have been used for the proposed approach. The thermal camera is used for social distancing and facial temperature measurement, while a visible camera is used for mask detection. The proposed method has been executed on NVIDIA platforms to assess algorithmic performance. For evaluation of the trained models, accuracy, recall, and precision have been measured. We obtained promising results for real-time detection for human recognition. Different couples of thermal and visible cameras and different NVIDIA edge platforms have been adopted to explore solutions with different trade-offs between cost and performance. The multi-feature algorithm is designed to monitor the individuals continuously in the targeted environments, thus reducing the impact of COVID-19 spread.