MONITORING COVID-19 SOCIAL DISTANCING WITH PERSON DETECTION AND TRACKING VIA FINE-TUNED YOLO V3 AND DEEPSORT TECHNIQUES
Author(s):
K. Abhishek Reddy, P. Sandeep, D. Gayathri
Keywords:
COVID-19, OpenCV, Social distancing, Deep learning, Computer vision, CCTV, Drones.
Abstract
Recently, the outbreak of Coronavirus Disease (COVID-19) has spread rapidly across the world and thus social distancing has become one of mandatory preventive measures to avoid physical contact. This survey paper emphasizes on a surveillance method which uses Open-CV, Computer vision and Deep learning to keep a track on the pedestrians and avoid overcrowding. The implementation can be done using closed circuit television (CCTV) and Drones where the camera will detect the crowd with the help of object detection and compute the distance between them. The Euclidean distance between two people will be calculated in pixels and is compared with given standard distance and if it is observed to be less than the standard distance the local authorities or local police authorities will be notified. As the pandemic situation has taken over the world, social distancing is one of the major precautions which needs to be taken. As people come together in crowds, they are more likely to come into close contact with someone that has COVID-19 and hence World Health Organization has proposed a strict law for maintaining physical distance of 1 meter (3 feet) in every pair. Thus, to keep a track of the social distancing among the public this idea of social distancing detector emerged.
Article Details
Unique Paper ID: 154359

Publication Volume & Issue: Volume 8, Issue 11

Page(s): 1 - 5
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