Minimising The Spread of Covid-19 Using Yolo V3 Algorithm

Abstract

Outbreak of Coronavirus disease have dramatically increased throughout the world and forced the world to a global disaster. The government has taken several steps to control the spread of covid-19 such as vaccination, social distancing, quarantine and so on. Researchers introduced various methodologies to ensure social distancing for controlling the spread of covid-19. To increase the accuracy of existing systems and also effectively maintain the social distancing, this work proposes an idea in which the social distancing with covid people only maintained instead of maintaining social distancing with all the people. In the proposed idea, the movement of the people is monitored using the surveillance camera. After capturing the image, the social distance is calculated using Euclidean distance and a tracking technique is used to track the people who are covid positive. The proposed idea will produce more accurate findings and allow you to calculate real measurable units. Therefore, the proposed system will be helpful to identify, count and alert the people who are near to infected people. The proposed idea also identifies the people violates the social distancing protocol and identifies the number of people around the COVID positive patient to minimize the spread of the Corona virus. The proposed system will improve the accuracy by 98% for accurately predicting the social distancing only with the people who are positive