DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
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 othe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/21/7514 |
| _version_ | 1797549815806033920 |
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| author | Mahdi Rezaei Mohsen Azarmi |
| author_facet | Mahdi Rezaei Mohsen Azarmi |
| author_sort | Mahdi Rezaei |
| collection | DOAJ |
| description | 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 <i>Computer Vision</i> and YOLOv4-based <i>Deep Neural Network</i> (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. |
| first_indexed | 2024-03-10T15:20:43Z |
| format | Article |
| id | doaj.art-68db4355c5dc42828a898df5872dd657 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:20:43Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-68db4355c5dc42828a898df5872dd6572023-11-20T18:31:16ZengMDPI AGApplied Sciences2076-34172020-10-011021751410.3390/app10217514DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 PandemicMahdi Rezaei0Mohsen Azarmi1Institute for Transport Studies, The University of Leeds, 34-40 University Road, Leeds LS2 9JT, UKFaculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranSocial 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 <i>Computer Vision</i> and YOLOv4-based <i>Deep Neural Network</i> (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.https://www.mdpi.com/2076-3417/10/21/7514social distancingCOVID-19human detection and trackingdistance estimationdeep convolutional neural networkscrowd monitoring |
| spellingShingle | Mahdi Rezaei Mohsen Azarmi DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic Applied Sciences social distancing COVID-19 human detection and tracking distance estimation deep convolutional neural networks crowd monitoring |
| title | DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic |
| title_full | DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic |
| title_fullStr | DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic |
| title_full_unstemmed | DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic |
| title_short | DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic |
| title_sort | deepsocial social distancing monitoring and infection risk assessment in covid 19 pandemic |
| topic | social distancing COVID-19 human detection and tracking distance estimation deep convolutional neural networks crowd monitoring |
| url | https://www.mdpi.com/2076-3417/10/21/7514 |
| work_keys_str_mv | AT mahdirezaei deepsocialsocialdistancingmonitoringandinfectionriskassessmentincovid19pandemic AT mohsenazarmi deepsocialsocialdistancingmonitoringandinfectionriskassessmentincovid19pandemic |