Cameras that recognise vehicles can control car-park access, check resident permits and replace in-vehicle toll devices. They also raise a fair question: where does all the video go?
The traditional design
In a traditional design, cameras stream video to a central system, which does the analysis. That needs bandwidth and storage, concentrates sensitive data in one place and makes it harder to explain what is kept and why.
Process at the edge
Edge AI turns this around. The recognition model runs on the camera itself. It reads the plate and sends only the result, such as the plate text, a timestamp and a location, to the command centre. The video does not leave the device.
The benefits follow directly:
- Less data to protect, because raw video is not stored centrally.
- Lower bandwidth and cost, because only small messages travel.
- A clearer purpose, because the system collects what the use case needs.
- Faster response, because decisions happen where the camera is.
This supports data protection requirements such as GDPR, although each deployment still needs its own assessment of retention, access and lawful purpose.
What good projects add
Recognition accuracy has to be measured in real conditions, including weather, light and plate variations, and including local formats such as Greek plates. People need a path to review uncertain reads and disputed cases. Retention and access should be set by policy and enforced by the system.
How we approach it
We build vehicle-recognition applications for parking, access control, municipal permit checks and toll automation, with deep-learning models and support for Greek plates. See our Transport & Logistics page.