Near Real-Time Social Distance Estimation in London
This work provides prompt and accurate data on social distancing for policy makers at the Greater London Authority to inform safe policy decisions during the COVID-19 pandemic.
This paper addresses the challenge of estimating social distancing adherence in London using live traffic camera feeds. The authors developed a framework that enables near immediate sampling and contextualization of activity and physical distancing, which is actively deployed on over 900 real-time feeds.
During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt and accurate data sources. Large well-defined heterogeneous compositions of activity throughout the city are sometimes difficult to acquire, yet are a necessity in order to learn 'busyness' and consequently make safe policy decisions. One component of our project within this space is to utilise existing infrastructure to estimate social distancing adherence by the general public. Our method enables near immediate sampling and contextualisation of activity and physical distancing on the streets of London via live traffic camera feeds. We introduce a framework for inspecting and improving upon existing methods, whilst also describing its active deployment on over 900 real-time feeds.