Open AccessSurvey on Social Distance Analyser Using Deep Learning Techniques
Author(s) -
Shrikant A. Shinde,
Sakshi Bondre,
Pratiksha Yewalkar,
Khushboo Patil,
Priyanaka Parmar
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-2516
Subject(s) - social distance , covid-19 , pandemic , computer security , population , vulnerability (computing) , computer science , internet privacy , business , medicine , environmental health , infectious disease (medical specialty) , disease , pathology
Covid – 19 Virus spreading over day by day, throughout the world, billions of people are infected. "Social distance" is the only key to decreasing the COVID 19 rate. The best way to deal with such a dangerous situation is to follow the rules made by the government. Maintaining social distancing is only the option to control the spread of the diseases. The ongoing COVID-19 virus outbreak has caused a global disaster with its deadly spreading. Due to the absence of effective remedial agents and the shortage of immunizations against the virus, population vulnerability increases. Social distancing is thought to be an adequate precaution (norm) against the spread of the pandemic virus. The risks of virus spread can be minimized by avoiding physical contact among people. To effectively control the Covid-19 virus situation, the survey relied on twelve research papers focusing on detecting social gap violators using in-depth learning algorithms. The goal is to detect violations of the social distancing rule. Many studies have been done and various innovative methods have been suggested. The purpose of this paper is to compare the proposed methods and their results. This survey paper will discuss the technologies used for this in detail.