A Deep Learning Algorithm to Monitor Social Distancing in Real-Time Videos: A Covid-19 Solution

• Main Authors: Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/37649/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163796024&doi=10.1007%2f978-3-031-08637-3_4&partnerID=40&md5=1c5cdb2fe3629d21262ab7eae3bfb6b9

Coronavirus disease in 2019 (COVID19) was caused by severe acute coronavirus syndrome 2. (SARS-CoV-2). It was found in December 2019 in Wuhan, Hubei, China, and has since spread to the rest of the world, causing the latest pandemic. By the 23rd of August 2020, over 23.3 million accidents have been registered in 188 countries and territories, resulting in over 806,000 fatalities. About 15 million people have been rehabilitated. Popular symptoms include coughing, toxins, tiredness, shortness of breath, and a loss of smell and taste. Keeping a healthy distance is the most important way to prevent the spread of this virus. The word �public-health social distance� applies to a category of non-pharmaceutical procedures or programs that are intended to prevent the spread of infectious disease by maintaining a physical distance between individuals. To our knowledge, there is no social distancing tool that can be used to detect social distancing follow-up in real-time images. In this chapter, we introduce our computer-vision-based social distancing tool, which can be used to monitor the follow-up of social distancing in real-time photographs. The findings of the real-time social distancing video can be seen at https://github.com/ahmadusmani12/Tutorials. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.