Open Access

ARTICLE

Primary Contacts Identification for COVID-19 Carriers from Surveillance Videos

by R. Haripriya^*, G. Kousalya

Department of Computer Science and Engineering, Coimbatore Institute of Technology, Coimbatore, 641014, India

* Corresponding Author: R. Haripriya. Email:

Computer Systems Science and Engineering 2022, 43(3), 947-965. https://doi.org/10.32604/csse.2022.024149

Received 06 October 2021; Accepted 30 November 2021; Issue published 09 May 2022

Abstract

COVID-19 (Coronavirus disease of 2019) is caused by SARS-CoV2 (Severe Acute Respiratory Syndrome Coronavirus 2) and it was first diagnosed in December 2019 in China. As of 25th Aug 2021, there are 165 million confirmed COVID-19 positive cases and 4.4 million deaths globally. As of today, though there are approved COVID-19 vaccine candidates only 4 billion doses have been administered. Until 100% of the population is safe, no one is safe. Even though these vaccines can provide protection against getting seriously ill and dying from the disease, it does not provide 100% protection from getting infected and passing it on to others. The more the virus spreads; it has more opportunity to mutate. So, it is mandatory to follow all precautions like maintaining social distance, wearing mask, washing hands frequently irrespective of whether a person is vaccinated or not. To prevent spread of the virus, contact tracing based on social distance also becomes equally important. The work proposes a solution that can help with contact tracing/identification, knowing the infected persons recent travel history (even within the city) for few days before being assessed positive. While the person would be able to give the known contacts with whom he/she has interacted with, he/she will not be aware of who all were in proximity if he/she had been in public places. The proposed solution is to get the CCTV (Closed-Circuit Television) video clips from those public places for the specific date and time and identify the people who were in proximity—i.e., not followed the safe distance to the infected person. The approach uses YOLO V3 (You Only Look Once) which uses darknet framework for people detection. Once the infected person is located from the video frames, the distance from that person to the other people in the frame is found, to check if there is a violation of social distance guideline. If there is, then the people violating the distance are extracted and identified using Facial detection and recognition algorithms. Two different solutions for Face detection and Recognition are implemented and results compared—Dlib based models and OpenCV (Open Source Computer Vision Library) based models. The solutions were studied for two different CCTV footages and the results for Dlib based models were better than OpenCV based models for the studied videos.

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