VESU learns the camera before it claims the road.
A traffic camera is not useful to VESU until the system understands what it is looking at. Is the visible pavement mainline, shoulder, ramp, median, work zone, or gore area? Which direction does traffic move? What recurring objects should not be mistaken for hazards? VESU learns that context from the feed itself.
Manual per-camera annotation does not scale.
Drawing lane masks and shoulder polygons for every camera is slow, expensive, and stale as soon as a camera moves or the work zone changes. A system that requires a human to draw every region before it can work will never cover the fleet.
Operators draw polygons for roadway, shoulder, gore, median. Configure lane masks. Set expected direction. Re-do it when the camera moves.
Specialty segmentation, lane detection, motion analysis, and vision-language labeling produce a versioned scene model — automatically, with confidence and maturity.
Focused perception. Not a VLM mega-prompt.
VESU does not ask a generic language model to infer pixel geometry from scratch. It uses each model for what that model is good at — and assigns meaning only when the visual evidence supports it.
Identifies candidate regions. Roadway, shoulder, sky, structure. Specialty models, not a generic prompt.
Lane detection and optical-flow analysis. Geometry and direction come from the right tools.
Used where it is strongest: semantic labeling over structured visual evidence. Not pixel-level geometry guessing.
Maturity and confidence per field. Stored, versioned, and referenced by every downstream stage.
A scene model has states, not just a value.
Not every camera goes from feed to production-grade detection on day one. VESU exposes maturity explicitly — and uses it to gate what claims it is willing to make.
Cameras move. VESU treats that as a state change, not a nuisance.
If the current view no longer matches the learned scene, VESU degrades coverage, triggers re-learning where appropriate, and records that previous claims no longer apply to the current view.
Each new frame's structure is compared to the learned scene. A divergence above threshold flags a possible view change.
When divergence persists, coverage state moves to degraded or gap. Incidents are gated until the scene is reconciled.
If the new view is a known preset, it switches to the matching scene model. Otherwise, re-learning begins; the old claims are not silently re-applied.
Bring a feed. We will learn the camera.
A scene model is not a luxury feature — it is what makes every downstream claim trustworthy. We are happy to walk through a learned scene on one of your cameras.