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Great Streets + Department of Transportation + Information Technology Agency - Count Machine

About

The City currently does bicycle and pedestrian counts via having a person manually the number of cyclists and pedestrians that go through an intersection via a video capture.

However, thanks to advances in Machine Vision technology, we could now automate that, allowing us to constantly count the number of pedestrians and cyclists, rather than only doing a 24 hour sample.

This project is a proof of concept of how we could count the number folks moving through our streets.

Sponsors

Jeanne Holm, ITA

Great Streets - WHO?

DOT - Seleta Reynolds, Marcel Porras

Partners

CSU LA, Dr. Mohammad Pourhomayoun

City Team

Hunter Owens

Goals

Long term, allow us to know annual active transportation counts for key corridors.

Deliverables

Using the Google Machine Vision API or similar algorithm, take data from video feeds and produce bicycle and pedestrian counts in the same format we got before.

Data Sources

  • Video Data (see s3://traffic-video-lacity/). To download the video data, install the AWS Command Line Tools and run aws s3 cp --recursive s3://traffic-video-lacity/ /local/path/for/data.
  • Count Ouput data (see data directory)

About

A pedestrian and bike counting service.

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  • Jupyter Notebook 74.9%
  • Python 25.1%