SOCIAL DISTANCING DETECTOR USING DEEP LEARNING

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Anand Kumar
Bhawna Patle
Swaraj Hirwani

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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How to Cite
Anand Kumar, Bhawna Patle, & Swaraj Hirwani. (2021). SOCIAL DISTANCING DETECTOR USING DEEP LEARNING. International Journal of Innovations in Engineering Research and Technology, 8(06), 39-42. https://doi.org/10.17605/OSF.IO/3YV8P
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Articles

How to Cite

Anand Kumar, Bhawna Patle, & Swaraj Hirwani. (2021). SOCIAL DISTANCING DETECTOR USING DEEP LEARNING. International Journal of Innovations in Engineering Research and Technology, 8(06), 39-42. https://doi.org/10.17605/OSF.IO/3YV8P

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