Real-Time Social Distancing Detector Using Socialdistancingnet-19 Deep Learning Network

7 Pages Posted: 11 Aug 2020

See all articles by Rinkal Keniya

Rinkal Keniya

K. J. Somaiya College of Engineering

Ninad Mehendale

K. J. Somaiya college of Engineering; Ninad's research Lab

Date Written: August 7, 2020

Abstract

With no doubt, the COVID-19 pandemic has put the world to a halt. The world we lived in a few months prior is completely different than what it is now. The virus is spreading quickly and is a danger to the human race. Seeing the necessity of the hour one must always take certain precautions of which one being social distancing. Maintaining social distancing during COVID-19 is a must to ensure a slowdown in the growth rate of new cases. Our manuscript focuses on detecting if the people around are maintaining social distancing or not. Using our own self developed model named SocialdistancingNet-19 for detecting the frame of a person and displaying labels, they are marked as safe or unsafe if the distance is less than a certain value. This system can be used for monitoring people via video surveillance in CCTV. Our model achieved an accuracy of 92.8 %.

Keywords: Social distancing , Object detection, COVID

JEL Classification: I

Suggested Citation

Keniya, Rinkal and Mehendale, Ninad and Mehendale, Ninad, Real-Time Social Distancing Detector Using Socialdistancingnet-19 Deep Learning Network (August 7, 2020). Available at SSRN: https://ssrn.com/abstract=3669311 or http://dx.doi.org/10.2139/ssrn.3669311

Rinkal Keniya

K. J. Somaiya College of Engineering ( email )

India

Ninad Mehendale (Contact Author)

Ninad's research Lab ( email )

M.G. Road, Naupada Thane
Thane, 400602
India

K. J. Somaiya college of Engineering ( email )

Mumbai, MA Maharashtra 400007
India

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