# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
###### 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-m physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a hybrid _Computer Vision_ and YOLOv4-based _Deep Neural Network_ (DNN) model for automated people detection in the crowd in indoor and outdoor environments using common CCTV security cameras. The proposed DNN model in combination with an adapted inverse perspective mapping (IPM) technique and SORT tracking algorithm leads to a robust people detection and social distancing monitoring. The model has been trained against two most comprehensive datasets by the time of the research--the Microsoft Common Objects in Context (MS COCO) and Google Open Image datasets. The system has been evaluated against the Oxford Town Centre dataset (including 150,000 instances of people detection) with superior performance compared to three state-of-the-art methods. The evaluation has been conducted in challenging conditions, including occlusion, partial visibility, and under lighting variations with the mean average precision of 99.8% and the real-time speed of 24.1 fps. We also provide an online infection risk assessment scheme by statistical analysis of the spatio-temporal data from people's 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 developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields such as autonomous vehicles, human action recognition, anomaly detection, sports, crowd analysis, or any other research areas where the human detection is in the centre of attention. social distancing; COVID-19; human detection and tracking; distance estimation; deep convolutional neural networks; crowd monitoring; pedestrian detection; inverse perspective mapping +
Footnote †: journal:  
appropriate medications or vaccines for the deadly virus, no definite success has been reported at the time of this research, and there is no certain treatment or recommendation to prevent or cure this new disease. Therefore, precautions are taken by the whole world to limit the spread of infection. These harsh conditions have forced the global communities to look for alternative ways to reduce the spread of the virus. Social distancing, as shown in Figure 1a, refers to precaution actions to prevent the proliferation of the disease, by minimising the proximity of human physical contacts in covered or crowded public places (e.g., schools, workplaces, gyms, lecture theatres, etc.) to stop the widespread accumulation of the infection risk (Figure 1b). For several months, the World Health Organisation believed that COVID-19 was only transmittable via droplets emitted when people sneeze or cough and the virus does not linger in the air. However, on 8 July 2020, the WHO announced:  
_\"There is emerging evidence that COVID-19 is an airborne disease that can be spread by tiny particles suspended in the air after people talk or breathe, especially in crowded, closed environments or poorly ventilated settings\"_[2]. Therefore, social distancing now claims to be even more important than thought before, and one of the best ways to stop the spread of the disease in addition to wearing face masks. Almost all countries are now considering it as a mandatory practice. According to the defined requirements by the WHO, the minimum distance between individuals must be at least 6 feet (1.8 m) in order to observe an adequate social distancing among the people [3]. Recent research has confirmed that people with mild or no symptoms may also be carriers of the novel coronavirus infection [4].