Social Distancing Detection in Real Time using Deep Learning and IoT

Abstract

Abstract: Social Distancing is the practice of maintaining a minimum amount of physical distance from your surrounding people or avoiding direct contact with people or objects in public places so as to minimize the exposure or transmission of diseases like COVID-19. This can also be coupled with wearing of face masks and regular sanitization of hands. The government has made social distancing norms compulsory and to be followed, and penalties would be imposed if violated. But there are a lot of examples of social distancing violations in public places due to negligence of people. There are approaches using bluetooth and mobile phones but it requires an app to be installed. This study provides a method for determining whether the social distancing rule has been broken by combining machine learning models and object detecting techniques. Appropriate actions would be taken in case of any violations detected. In this paper, a comparison is done on all the object detection techniques like Faster R-CNN, YOLO v3, SSD Mobile Net etc. based on accuracy, mean average precision, computational time and ease of integration. Based on this, SSD Mobile Net performed well on accuracy and had a faster computational time. This model can also be deployed on end devices like Raspberry Pi.