Open AccessSocial Distancing Detector using Image Processing
Author(s) -
Aman Kumar Sao,
Harish Khedekar,
Chirag Panpaliya,
Shantanu Korde,
Kavita S. Kumavat
Publication year - 2021
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-1062
Subject(s) - social distance , pandemic , bounding overwatch , task (project management) , distancing , computer science , covid-19 , computer security , artificial intelligence , political science , medicine , economics , disease , management , pathology , infectious disease (medical specialty)
The lack of public awareness and negligence, the pandemic due to coronavirus(covid19) has brought a global crisis with its deadly spread to more than 180 countries, and about 147 million confirmed cases along with 3.11 million deaths globally as of 26th April 2021. Due to the absence of the vaccine against the covid19 the world has suffered a lot. Though scientists have developed several vaccines then also the pandemic is still out of control so therefore the only feasible option available to us is social distancing. And this notion motivated us to bring up the idea of a social distancing detector using image processing which includes a deep learning framework for automation task monitoring. The framework utilizes the YOLO v3 model object detection model to separate moving people from the background and to detect people by using bounding boxes. The basic idea of this article is to analyze the social distancing violation index rate that how many people violate the rule of social distancing in a particular interval of time.