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

Social distancing is one of the most important measures to prevent the spread of COVID-19. CCTV cameras may be used to track whether people are following the recommendation of 2-meter minimum distance between individuals in public places.

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A recent study suggests a technology based on deep neural networks to detect people, track them, and estimate the distances. This system may be used in different lighting and visibility conditions and can be applied on different types of CCTV cameras with any resolution.

By analysing the movement of people, it is possible to determine the number of people who violate the social-distancing measures, the time of the violations for each person and to identify the zones of highest risk. This technology can also be applied in other surveillance security, pedestrian detection, or autonomous vehicles systems.

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 generic 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 model includes a YOLOv4-based framework and inverse perspective mapping for accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online 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 infections. 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.