Urban Edge Vision
Network cameras are set up in a web console. A vision-language model running on the device looks at each frame, use-case packs turn what it sees into traffic events, and an operator reviews them. Processing stays on the device, and nothing is enforced automatically.
Four packs, drawn on the street
Each pack works on geometry the operator draws in the Studio page over a live snapshot from the camera. Coordinates are stored normalized to the frame, so they survive a change of stream resolution.
- 1Stop sign
Watches each vehicle track through the stop zone and calls it a full stop, a rolling stop or no stop from its dwell time and slowest speed in the zone.
- 2Speed between two gates
Times a track from gate A to gate B and converts the distance the operator entered into km/h or mph. Only measured crossings produce a reading.
- 3Vehicle count
Counts each tracked vehicle once as it crosses the A to B line, by direction and by class: car, truck, bus, motorcycle. Hourly totals per camera.
- 4Moving object
Tracks people or other chosen classes with direction and a short descriptor, across the whole frame or inside a drawn zone.
Speed and stop sign are refused on the same camera. They need different sight lines, so the console blocks the combination instead of producing weak results for both.
From stream to reviewed event
One capture thread per camera writes the newest frame to a single slot.
The browser reads that slot as MJPEG at the camera's own frame rate.
Inference samples the slot every interval. vLLM, Ollama or NVIDIA NIM, switchable live.
Detections get stable track ids, then each pack bound to the camera applies its rule.
Events persist in SQLite. Flagged ones wait in Review with the frame the model saw.
The model server is not in the video path. If it stops or falls behind, the Live page keeps playing and shows "inference unavailable" instead of inventing events. An end-to-end runtime test checks exactly that.
What each pack actually decides
The defaults below are read from the pack code. Every one can be changed per camera in Studio.
Stop sign
- Decided once, when the track leaves the zone
- Rolling and no stop go to the Review queue
Speed between two gates
- Distance comes from a tape measure or a map, entered once
- Readings shown in km/h or mph
Vehicle count
- Direction labels are the operator's, such as Eastbound
- Counts never enter the Review queue
Moving object
- Useful near crosswalks and sidewalks
- Works on any camera, street view or not
Set up, watch and review in one place
Screens from the running app. These captures use the built-in synthetic test feed and the mock detector, and the console labels both on screen so they are never mistaken for a real camera.
Details that matter once it is on a pole
Camera passwords are encrypted at rest with a key file outside the repository. The API never returns one, and a saved password is only reused for the host and port it was saved for.
Stream URLs are masked in logs, errors, status payloads and run artifacts.
On first start the console picks a default for the hardware: Cosmos-Reason2-2B on Thor, Gemma 4 on a workstation. The operator can switch at any time.
A model container another app depends on cannot be stopped by accident. Stopping it needs an explicit, logged confirmation.
A systemd unit starts the API and console at boot and keeps a persistent log.
Packs flag, operators confirm or dismiss. Each item can carry a ground-truth note, such as "my car, full stop", so pack output can be checked against known passes.
What is proven, and what is next
- Runs end to end from camera setup to the Review queue, on the Thor and on a workstation.
- 271 automated tests pass covering camera setup, connection probing, credential handling, model switching and memory preflight, all four packs and the review flow.
- CI on every push: lint, type checks, tests and the console build.
- Live-camera latency and power on the Thor. No live-camera run is committed yet.
- Pack accuracy against known passes: a car at a known speed, a full stop, a rolling stop.
- Speed calibration error, which depends on gate placement, the entered distance and the inference interval.
- Long-run stability over hours and days, and behaviour at night or in rain.
Out of scope by design: license plate reading and automated ticketing. This is operational analysis with a person in the loop.