2020/12/07 by James A. Walsh, James Walsh, Oluwafunmilola Kesa +17 · 1 voice
Computer Science · Social Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Video Surveillance and Tracking Methods #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.2012.07751
openalex publication_date 2020/12/07 · arxiv published 2020/12/07 · arxiv updated 2022/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.