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.
Highlights
The novel generation of the coronavirus disease (COVID-19) was reported in late December 2019 in Wuhan, China
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
We proposed a Deep Neural Network-Based human detector model called DeepSOCIAL to detect and track static and dynamic people in public places in order to monitor social distancing metrics in COVID-19 era and beyond
Summary
The novel generation of the coronavirus disease (COVID-19) was reported in late December 2019 in Wuhan, China. Recent research has confirmed that people with mild or no symptoms may be carriers of the novel coronavirus infection [4] It is important all individuals maintain controlled behaviours and observe social distancing. 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 copping with COVID-19 challenges and successful social distancing practices. These works vary from GPS-based patient localisation and tracking to segmentation, and crowd monitoring.
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