Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

Version 1 : Received: 16 September 2020 / Approved: 17 September 2020 / Online: 17 September 2020 (11:57:01 CEST)

How to cite: Rezaei, M.; Azarmi, M. DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic. Preprints 2020, 2020090404 (doi: 10.20944/preprints202009.0404.v1). Rezaei, M.; Azarmi, M. DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic. Preprints 2020, 2020090404 (doi: 10.20944/preprints202009.0404.v1).

Abstract

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a Deep Neural Network-based Model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed DNN model along with an inverse perspective mapping technique leads to a very accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online infection risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

Supplementary and Associated Material

https://youtu.be/FwCP2ySDshE: All Outcomes of the Research in one Window
https://youtu.be/nPKJKtS-owA: People Detection and Tracking
https://youtu.be/UHc275F1djk: Live Crowd Map of Social Distancing Violations

Subject Areas

Social Distancing; COVID-19; Human Detection and Tracking; Distance Estimation, Deep Convolutional Neural Networks; Crowd Monitoring, Inverse Perspective Mapping

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