ABSTRACT
The COVID-19 pandemic presents an unprecedented challenge to public health, food systems and the demand and supply chains. 'Coronavirus' spreads when an infected person coughs, sneezes or talks, and droplets from their mouth are launched into the air and inhaled by people in the vicinity. Mid-2021 witnessed the production and supply of effective vaccines against Coronavirus, and around 4.5 billion vaccine doses have been utilised globally, reducing fatalities significantly. Given the Government's plans to ease quarantine restrictions for schools, offices, and public places, Social Distancing has become even more critical than ever before. This project incorporates Computer Vision techniques using the high-performance YOLOv4 library, DSFD Face detector, Deep Learning Darknet and Pre-trained ResNet models, and RaspberryPi to create a plug-and-play extension for CCTV cameras established in public places. The system uses the frame by frame information of CCTVs to detect people and classify violations of Social Distancing norms. The device also performs real-time Face Mask Detection, and this technique is robust to varying geometries of face masks and degrees of natural illumination. In case of a detected violation of Social Distancing norms, a buzzer blares in the background. The timestamp of violation with the snapshot of the frame highlighting the associated people is sent to a database and emailed to a centralised server for further investigation. © 2021 IEEE.All rights reserved.
ABSTRACT
COVID-19 has been announced as a Global Communal Health Extremity by WHO on January 2020. Meaningful preventive solutions have been taken with smartphone selfie/geofencing apps toward managing mandatory home quarantine and physical distancing. In the post-COVID world, fast screening and strict quarantine can play a crucial role in bringing back normality. Quarantine being offered at home can be a comfortable solution for both government and patients. On the other hand, it can be hazardous if not strictly followed and adequately realized. However, the existing geofencing/face selfie apps take static photographs and location data at certain time intervals that can allow patients to violate the rules between those periods, thus failing to ensure active user identity. To realize unbreached home quarantine policies, this article introduces a CUBA-HQM smartphone app that performs continuous user biometric authentication (CUBA) augmented with geofencing using AI technology. The purpose of continuous tracking is to strictly control the spread of infectious diseases in society by monitoring the individual move in/out in the quarantine zone.