Abstract
Social distance has been crucial in preventing the rapid spread of the COVID-19 pandemic since the outbreak began. For public spaces to be safe places where people's social distancing is respected, most methods of social distancing consist of the following steps: the system (i) monitors and analyzes the actual distances between people in real-time; (ii) looks for violations of social distancing among the crowds; and (iii) collects information on and alerts those who violate the rules. This paper provides the first review of the substantial focus of convolutional neural networks (CNN)-based techniques. Also been conducted to identify their advantages and disadvantages. This article examines the most fundamental ideas about social distancing. The methods and knowledge of the basic algorithms used by previous researchers and studies, as well as the data set that can be used to evaluate algorithms that can tell when a distance is safe.
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