Privacy Preserving Monitoring Of Social Distancing In Public Environments Machine Learning, Computer Vision, Social Distancing, GDPR by design (PEOPLE)
- Funded by Luxembourg National Research Fund
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Known Financial Commitments (USD)
$53,136Funder
Luxembourg National Research FundPrincipal Investigator
Raphael FrankResearch Location
LuxembourgLead Research Institution
University of LuxembourgResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Impact/ effectiveness of control measures
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
The aim of this project is to provide a platform to run a comprehensive analysis on the Social Distancing measures decided by the government in the context of the COVID-19 pandemic. To do so we propose to analyse anonymised video data in the city of Luxembourg. The first step will be to anonymise the video feed by using well known Artificial Intelligence (AI) models (face blurring). In a next step will use other AI models to identify pedestrians and groups of individuals, calculate their relative distances and overall density. Those metrics can then be evaluated over time for different locations and provide valuable insights on the greater or lesser risks of infection spreading based on behaviour. The rules can be used either to inform where the police need to focus their efforts in enforcing rules, or to inform and influence the public's actions (or both).