ABSTRACT
The coronavirus (COVID-19) is a lethal virus causing a rapidly infec-tious disease throughout the globe. Spreading awareness, taking preventive mea-sures, imposing strict restrictions on public gatherings, wearing facial masks, and maintaining safe social distancing have become crucial factors in keeping the virus at bay. Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus, the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly. To fight the spread of this virus, technologically developed systems have become very useful. However, the implementation of an automatic, robust, continuous, and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community. This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system. A modified version of a convolutional neural network, the ResNet50 model, has been utilized to identify masked faces in peo-ple. You Only Look Once (YOLOv3) approach is applied for object detection and the DeepSORT technique is used to measure the social distance. The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system, Jetson Nano edge computing device, and smartphones, Android and iOS applications. Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores. © 2023, Tech Science Press. All rights reserved.
ABSTRACT
Social distancing is of key importance during the current pandemic. It helps limit the spread of COVID by observing distance between disease spreading individuals. Now it is not possible to station a person 24x7 at each queue to monitor social distancing violations. Banks, Public Offices, Malls, Schools, Theatre, etc.., usually see long queues for hours every day. To ensure social distancing in queues we hereby design a social distancing monitoring robot. The robot consists of a four wheel design system used to drive the robotic vehicle. It makes use of a line following principle to constantly move along with the queue and monitor for social distancing violations. The robot use IR sensor to travel along with the queue to and front in order to detect violations. The robot is now equipped with the obstacle detecting ultrasonic sensor in order to detect obstacles in the vehicle path. The robotic vehicle uses other ultrasonic sensor for detecting distance between two individuals in a queue. It any two individuals are found having less than three feet distance between them, the robot instantly sounds a buzzer and alert to inform about the violation, also it sends alerts of these violations along with a camera picture using WiFi over IoT to inform the higher authorities or head office to update them about violations with proof so instant disciplinary action can be taken. Thus this work allows for automatic maintaining social distancing in queues help to prevent the spread of the Corona virus