Vehicle Detection Using YOLO and Mobility Tracking During COVID-19 Pandemic Lockdowns

Vehicle Detection Using YOLO and Mobility Tracking During COVID-19 Pandemic Lockdowns

Data-driven approaches to traffic monitoring have important applications in tracking vehicle mobility in the COVID-19 pandemic lockdowns. We report preliminary results of a pipeline that uses the You Only Look Once (YOLOv3) and the Simple Online and Realtime Tracking (SORT) algorithms to count and c...

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Bibliographic Details
Main Authors: Tan, Marion Ivan L, Calgo, Clyde J, Cabantac, Sheanne Eric P, Honrado, Jaime Luis E, Libatique, Nathaniel Joseph C, Tangonan, Gregory L
Format: text
Published: Archīum Ateneo 2021
Subjects:
COVID-19
Pandemics
Vehicle detection
Conferences
Pipelines
Internet
Classification algorithms
traffic videos
object detection
COVID-19 pandemic
SARS-COV-2
lockdown
community mobility
Electrical and Computer Engineering
Transportation
Online Access:https://archium.ateneo.edu/ecce-faculty-pubs/120
https://ieeexplore.ieee.org/document/9612481
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Institution: Ateneo De Manila University
Description
Summary:Data-driven approaches to traffic monitoring have important applications in tracking vehicle mobility in the COVID-19 pandemic lockdowns. We report preliminary results of a pipeline that uses the You Only Look Once (YOLOv3) and the Simple Online and Realtime Tracking (SORT) algorithms to count and classify vehicles in traffic videos. We correlate vehicle counts in Katipunan Avenue, Metro Manila and Google COVID-19 Community Mobility Reports from May to August 2020 and we show that vehicle detection data may be considered for monitoring community response to changes in COVID-19 lockdown stringency levels.

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