Title: COVID-19 suspected person detection and identification using thermal imaging-based closed circuit television camera and tracking using drone in Internet of Things
Authors: Pawan Singh Mehra; Yogita Bisht Mehra; Arvind Dagur; Anshu Kumar Dwivedi; M.N. Doja; Aatif Jamshed
Addresses: Department of CSE, Delhi Technological University, New Delhi, India ' Department of Kayachikitsa, G.S. Ayurveda Medical College and Research Centre, Hapur, Uttar Pradesh, India ' Department of CSE, Krishna Engineering College, Ghaziabad, Uttar Pradesh, India ' Department of CSE, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India ' Department of CSE, Indian Institute of Information Technology, Sonepat, Haryana, India ' Department of Information Technology, ABES Engineering College, Ghaziabad, Uttar Pradesh, India
Abstract: COVID-19 has emerged as a worldwide health concern. It is very hard to check or scan every individual. In this paper, we have propounded a system where the suspected person can be easily detected and identified for COVID-19 by using thermal imaging-based closed-circuit television. The thermal imaging-based closed-circuit television will automatically scan the person in vicinity and capture the video/image of the suspected person. The system will raise an alarm in the vicinity so that people in vicinity keep clear of each other. The recorded video/image will be forwarded to the base station and information about the suspected person will be fetched from the server. Meanwhile, the drones will be used for tracking the suspected person until the nodal medical team diagnoses the suspected person for confirmation. The proposed system can contribute significantly to curbing the rate of infected COVID-19 persons and prevent further spread of this pandemic disease.
Keywords: coronavirus; COVID-19; face recognition; drone; Internet of Things; automation; deep learning.
International Journal of Computer Applications in Technology, 2021 Vol.66 No.3/4, pp.340 - 349
Received: 06 Jul 2020
Accepted: 13 Sep 2020
Published online: 21 Jan 2022 *