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Traffic Monitor

TrafficMonitor.ai built with edge ML for object detection and radar for speed monitoring

glossyioglossyio
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TrafficMonitor.ai, the Traffic Monitor, is an open source smart city traffic monitoring software built with commodity hardware to capture holistic roadway usage. Utilizing edge machine learning object detection and Doppler radar, it counts pedestrians, bicycles, and cars and measures vehicle speeds.

Site: https://trafficmonitor.ai
Repo: https://github.com/glossyio/traffic-monitor
Docs: https://docs.trafficmonitor.ai
Chat: https://trafficmonitor.zulipchat.com/

Our mission is to democratize the power of AI tools to improve community safety and quality of life through the ethical and transparent use of these capabilities. The Traffic Monitor software and hardware are open source and available for anyone to build, modify, improve, and contribute back. We welcome your ideas and contributions at the Traffic Monitor GitHub repository!

Note: This project is in early-beta.  Updates to the software will likely include major, breaking changes without a way to reverse. We appreciate your patience and welcome contributions!

v0.3.0 has been released!  This update brings improved stability, many new metrics and telemetry, a new configuration system supporting any number of cameras and sensors to be pre-configured, air quality monitoring capabilities, and more!  Read the release announcement for full details.

tm-ondevice-dash-w-aq-2025-02.png

On-device dashboard with Air Quality

Portable Network Graphics (PNG) - 657.34 kB - 03/10/2025 at 18:27

Preview

events-counts-daily.png

Cumulative events by object for the day (resets daily).

Portable Network Graphics (PNG) - 28.98 kB - 03/10/2025 at 18:23

Preview

events-zone_radar-last5.png

Last 5 events detected and confirmed by radar.

Portable Network Graphics (PNG) - 393.29 kB - 03/10/2025 at 18:23

Preview

event-car-speed-stats-daily.png

Car object speed statistics for the day (resets daily).

Portable Network Graphics (PNG) - 31.14 kB - 03/10/2025 at 18:24

Preview

events-hourly-24-hours.png

Hourly events count by object for the last 24-hours.

Portable Network Graphics (PNG) - 63.47 kB - 03/10/2025 at 18:23

Preview

View all 8 files

  • 1 × Raspberry Pi 5 RPi5 4GB or 8GB
  • 1 × AI co-processor Coral TPU or Raspberry Pi HAT+ (Hailo8L)
  • 1 × Doppler Radar(s) OmniPreSense OPS243-A Doppler Radar Sensor
  • 1 × Camera(s) Raspberry Pi Cam 3 (Wide Angle), 1 or more
  • 1 × Enclosure Weather-resistant, 3D printable model available in the docs

View all instructions

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