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Estimating vehicle and pedestrian activity from town and city traffic cameras

Lookup NU author(s): Daniel Bell, Professor Philip James, Dr Luke Smith

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Traffic cameras are a widely available source of open data that offer tremendous value to public authorities by providing real-time statistics to understand and monitor the activity levels of local populations and their responses to policy interventions such as those seen during the COrona VIrus Disease 2019 (COVID-19) pandemic. This paper presents an end-to-end solution based on the Google Cloud Platform with scalable processing capability to deal with large volumes of traffic camera data across the UK in a cost-efficient manner. It describes a deep learning pipeline to detect pedestrians and vehicles and to generate mobility statistics from these. It includes novel methods for data cleaning and post-processing using a Structure SImilarity Measure (SSIM)-based static mask that improves reliability and accuracy in classifying people and vehicles from traffic camera images. The solution resulted in statistics describing trends in the ‘busyness’ of various towns and cities in the UK. We validated time series against Automatic Number Plate Recognition (ANPR) cameras across North East England, showing a close correlation between our statistical output and the ANPR source. Trends were also favorably compared against traffic flow statistics from the UK’s Department of Transport. The results of this work have been adopted as an experimental faster indicator of the impact of COVID-19 on the UK economy and society by the Office for National Statistics (ONS).


Publication metadata

Author(s): Chen L, Grimstead I, Bell D, Karanka J, Dimond L, James P, Smith L, Edwardes A

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2021

Volume: 21

Issue: 13

Online publication date: 03/07/2021

Acceptance date: 25/06/2021

Date deposited: 12/07/2021

ISSN (electronic): 1424-8220

Publisher: MDPI AG

URL: https://doi.org/10.3390/s21134564

DOI: 10.3390/s21134564

Data Source Location: https://github.com/datasciencecampus/chrono_lens


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Funding

Funder referenceFunder name
EP/P016782/1EPSRC
EP/R013411/1EPSRC
NE/P017134/1Natural Environment Research Council (NERC)

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