Near Real-Time Social Distance Estimation in London

Autor:	Walsh, James, Kesa, Oluwafunmilola, Wang, Andrew, Ilas, Mihai, O'Hara, Patrick, Giles, Oscar, Dhir, Neil, Girolami, Mark, Damoulas, Theodoros
Rok vydání:	2020
Předmět:	Computer Science - Computers and Society Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Version accepted by The Computer Journal
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.07751 Zobrazit plný text záznamu View this record from Arxiv