Abstract: COVID-19 disease caused by a severe respiratory syndrome coronavirus. It was identified in December 2019 in Wuhan city, China. It’s resulted in an ongoing pandemic that caused infected cases including many deaths. The rampant coronavirus disease 2019 (COVID-19) has brought global crisis with its deadly spread to several countries. Due to large population and lesser availability of vaccines, to maintain social distancing is that the only one feasible approach to fight against this pandemic situation. To implement Social distancing, group activities such as functions, social gatherings, travelling, workshops and even praying of people are banned by government during quarantine period. Social distancing, also called physical distancing is the only effective measures taken to prevent the spread of contagious disease by maintaining physical distance between people and reducing the number of times people acquire close contact with one another. Today`s unfortunate circumstances due of spread COVID-19, keeping distance among people is crucial. The goal is to detect people using Haar cascade classifier and find the space between people to check whether a norm social distance of 50 inch is maintained by people. Hence, this work aims at minimizing the spread of the COVID-19 virus by evaluating if and the way persons go with social distancing rules.

Keywords: Covid-19, Social distancing, Python, OpenCV, Deep neural network.

PDF | DOI: 10.17148/IJARCCE.2021.105113

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