SOCIAL DISTANCING DETECTOR USING DEEP LEARNING

Authors

  • Anand Kumar Department of computer science, Govt Engineering College, Raipur, C.G.
  • Bhawna Patle Department of computer science, Govt Engineering College, Raipur, C.G.
  • Swaraj Hirwani Department of computer science, Govt Engineering College, Raipur, C.G.

DOI:

https://doi.org/10.17605/OSF.IO/3YV8P

Keywords:

Social distancing, Object detection, Crowd analysis

Abstract

This paper presents a methodology for detecting social distance using deep learning and computer vision between people to control the spread of covid-19. This application is developed to give alerts to people for maintaining social distance in crowded places. By using pre-recorded video as input and the open-source object detection pretrained model using the YOLOv3 algorithm We can tell if people are following social distancing or not and based on that we are creating red or green bounding boxes over it. It is also working on web cameras, CCTV, etc, and can detect people in real-time. This may help authorities to redesign the layout of public places or to take precautionary actions to mitigate high-risk zones.it can be used in other fields also like autonomous vehicles, human action recognition, crowd analysis.

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Published

2021-06-01

Issue

Vol. 8 No. 06 (2021): IJIERT

Section

Articles

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