Near Real-Time Social Distance Estimation in London

TL;DR

This paper presents a framework for near real-time estimation of social distancing adherence in London using live traffic camera feeds, aiding policymakers with timely activity data during the COVID-19 pandemic.

Contribution

It introduces a novel framework for analyzing live camera feeds to estimate social distancing, actively deployed on over 900 feeds in London.

Findings

Framework enables near real-time activity sampling.

Active deployment on 900+ live feeds in London.

Supports timely policy decisions during COVID-19.

Abstract

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.