Abstract

Inadequate pedestrian and cycling infrastructure in city streets puts non-motorists across the globe at fatal risk.
One reason for the severity of these public health and safety problems is a lack of accurate, representative data on
the usage of urban streets across transit modes. Unfortunately, attempts to fill this gap in understanding have been
limited over time by the prohibitively low reliability or high cost of current methods. Computer vision (CV)
systems offer a scalable and accurate platform with which to monitor traffic that overcome the sampling biases
imposed by most traffic measurement solutions. One such system is Numina, a standalone sensor and data platform
that leverages computer vision and machine learning to count and map the travel paths of street users in real time.
Numina can concurrently detect and track travellers representing any mode of traffic and performs the majority of
image processing onboard each device itself, resulting in anonymized traffic data that is protective of personal
privacy. Using Numina and similar CV systems to enable an iterative approach to urban planning can mitigate
some of the risks and costs associated with legacy traffic data collection methods.


Original document

The different versions of the original document can be found in:

https://zenodo.org/record/1491614 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
http://dx.doi.org/10.5281/zenodo.1491613 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode


DOIS: 10.5281/zenodo.1491614 10.5281/zenodo.1491613

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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.5281/zenodo.1491614
Licence: Other

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