# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
###### Abstract
Therefore, it is important all individuals maintain controlled behaviours and observe social distancing. Many research works such as [5; 6; 7] have proved social-distancing as an effective non-pharmacological approach and an important inhibitor for limiting the transmission of contagious diseases such as H1N1, SARS, and COVID-19. Figure 2 demonstrates the effect of following appropriate social distancing guidelines to reduce the rate of infection transmission among individuals [8; 9]. A wider Gaussian curve with a shorter spike within the range of the health system service capacity makes it easier for patients to fight the virus by receiving continuous and timely support from the health care organisations. Any unexpected sharp spike and rapid infection rate (such as the red curve in Figure 2), will lead to service failure, and consequently, exponential growth in the number of fatalities. During the COVID-19 pandemic, governments have tried to implement a variety of social distancing practices, such as restricting travels, controlling borders, closing pubs and bars, and alerting the society to maintain a distance of 1.6 to 2 m from each other [10]. However, monitoring the amount of infection spread and efficiency of the constraints is not an easy task. People require to go out for  
Figure 1: People detection, tracking, and risk assessment in Oxford Town Centre, using a public CCTV camera. (**a**) Social distancing monitoring; (**b**) Accumulated infection risk (red zones) due to breaches of the social-distancing. essential needs such as food, health care and other necessary tasks and jobs. Therefore, many other technology-based solutions such as [11; 12] and AI related research such as [13; 14; 15] have tried to step in to help the health and medical community in coping with COVID-19 challenges and successful social distancing practices. These works vary from GPS-based patient localisation and tracking to segmentation, and crowd monitoring. In such situations, Artificial Intelligence can play an important role in facilitating social distancing monitoring. Computer Vision, as a sub-field of Artificial Intelligence, has been very successful in solving various complex health care problems and has shown its potential in chest CT-Scan or X-ray based COVID-19 recognition [16; 17] and can contribute to Social-distancing monitoring as well. Besides, deep neural networks enable us to extract complex features from the data so that we can provide a more accurate understanding of the images by analysing and classifying these features. Examples include diagnosis, clinical management and treatment, as well as the prevention and control of COVID-19 [18; 19]. Possible challenges in this area are the importance of gaining a high level of accuracy, dealing with a variety of lighting conditions, occlusion, and real-time performance. In this work, we aim at providing solutions to cope with the mentioned challenges, as well. The main contribution of this research can be highlighted as follows:  
* This study aims to support the reduction of the coronavirus spread and its economic costs by providing an AI-based solution to automatically monitor and detect violations of social distancing among individuals. * We develop a robust deep neural network (DNN) model for people detection, tracking, and distance estimation called DeepSOCIAL (Sections 3.1-3.3). In comparison with some recent works in this area, such as [15], we offer faster and more accurate results. * We perform a live and dynamic risk assessment, by statistical analysis of spatio-temporal data from the people movements at the scene (Section 4.4). This will enable us to track the moving trajectory of people and their behaviours, to analyse the ratio of the social distancing violations to the total number of people in the scene, and to detect high-risk zones for short- and long-term periods.