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An open source tool to quantify the world

Installation guide
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About OpenDataCam

'OpenDataCam' is a tool that helps to quantify the world. It's here for everyone to use! With computer vision OpenDataCam understands and quantifies moving objects. The simple setup allows everybody to become an urban data miner.
How can OpenDataCam see?
OpenDataCam consists of a camera attached to a mini computer. It's running an object detection algorithm that counts and tracks moving objects. The board is equipped with a GPU (graphical processing unit), which allows it to process many parallel image threads at once. The perfect tool for image analysis and video processing.
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On the software side OpenDataCam is running YOLO — an object detection library. YOLO is trained to detect objects in images. The attached camera feeds YOLO with a video, YOLO then outputs all objects in each frame.
YOLO neural network
OpenDataCam is not tied to a specific housing, instead you can design your own or use an already existing casing. We used the Wildlife Cam Casing by Naturbytes to protect the hardware in the wild. Have a look at this tutorial to see how the set up the the Jetson NANO in the Wildlife Cam Casing.
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App demo

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OpenDataCam comes feature packed, the highlight are

  • Multiple object classes
  • Fine grained counter logic
  • Trajectory analysis
  • Real-time or pre-recorded video sources
  • Run on small devices in the field or data centers in the cloud
  • You own the data
  • Easy to use API


The discussion around the use of artificial intelligence in public space is ongoing. We believe OpenDataCam is a contribution to this discussion, as it offers an offline, transparent, thus privacy respecting alternative. Paramount to the open source access is the responsible use of this technology we request from every user.
As OpenDataCam is an experimental prototype with public facing applications, privacy is something we tried to bake into the design of the tool. After getting in touch with Sensor Labs privacy label, we’re currently finding ways to apply the label to OpenDataCam.

OpenDataCam in the Wild

After publishing the alpha version, we asked people what they did with it. These selected cases are here to show you what you can do with OpenDataCam.
Let us know about how you use OpenDataCam. Apart from that we also think OpenDataCam would be great to…Profiling in spaces like shops, train, stations, drones...
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Research Project with HTW & Technologiestiftung

In coordination with the Berlin traffic administration and advising planners, we'd like to explore how the OpenDataCam performs in real-world settings. We'll take a closer look on accuracy and test scenarios and data evaluation.

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Privacy-centred data-driven innovation in the smart city. Exemplary use case of traffic counting

This paper, shows the use case of traffic counting as an example to illustrate how the use of advanced technologies and integrated planning strategies can shift the balance between administrative and business interests on the one hand, and privacy concerns on the other, towards a privacy-centric approach. They demonstrate a privacy-centric participatory development process that led to a prototypical technical solution using OpenDataCam for privacy-friendly and human-centric traffic counting.

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Logistics NRW Dashboard

The “Crowd Solving” project has implemented a Logistics Dashboard including various webcam locations where OpenDataCam is supposed to be implemented to create traffic data around the Neuss Harbour area.

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Umsetzungsprojekt mit Stadt Hennef: Verkehrserfassung

In der praktischen Anwendung können so zum Beispiel vorbeilaufende Personen (bspw. im Einzelhandel) erfasst oder Fahrzeuge (bspw. zur Steuerung logistischer Prozesse) in einem bestimmten Bereich gezählt werden. Diese Daten könnten dann wiederum genutzt werden, um Prozesse anzustoßen, wie etwa die Anpassung von Reinigungsintervallen an tatsächliche Besucherzahlen oder der Versand automatischer Benachrichtigungen bei Ankunft oder Abfahrt eines Lieferfahrzeuges.


Valentin Sawadski
Project Lead
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OpenDataCam was initiated by move lab (Benedikt GroßMarkus KreutzerRaphael ReimannFlorian Porada, Neele Rittmeister) and Thibault Durand. It is developed by a small team of volunteers now.

See complete list of contributors on GitHub