  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 hybrid Computer Vision and
YOLOv4-based Deep Neural Network 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 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. 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.
