This paper is published in Volume-7, Issue-4, 2021
Area
Computer Vision
Author
Devanshi Gupta, Saumya Srivastava, Sonali P. Dash
Org/Univ
Bharati Vidyapeeth Deemed University College of Engineering, Pune, Maharashtra, India
Pub. Date
09 August, 2021
Paper ID
V7I4-1676
Publisher
Keywords
COVID-19, Social Distancing, Computer Vision, Birds Eye View, Camera Calibration

Citationsacebook

IEEE
Devanshi Gupta, Saumya Srivastava, Sonali P. Dash. Social distancing monitoring system using computer vision and YOLOv3, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Devanshi Gupta, Saumya Srivastava, Sonali P. Dash (2021). Social distancing monitoring system using computer vision and YOLOv3. International Journal of Advance Research, Ideas and Innovations in Technology, 7(4) www.IJARIIT.com.

MLA
Devanshi Gupta, Saumya Srivastava, Sonali P. Dash. "Social distancing monitoring system using computer vision and YOLOv3." International Journal of Advance Research, Ideas and Innovations in Technology 7.4 (2021). www.IJARIIT.com.

Abstract

In the fight against COVID-19, social separation has proven to be an extremely successful strategy for slowing the disease's transmission. People are being motivated to limit their interactions with one another to reduce the risk of the virus spreading through physical or close touch. In the past, AI/Deep Learning has shown promise in solving a variety of everyday problems. We shall see a full explanation of how we may utilize Python, Computer Vision, and Deep Learning to detect social distancing in public spaces and workplaces in this suggested system. By analyzing the real-time video streams from the camera, the social distancing detection tool can determine whether people have kept a safe distance from each other in public settings and the workplace. We can integrate this tool into their security video systems to check if people at work, in factories, and in stores are keeping a safe distance from one another.