Turn existing traffic cameras into autonomous, auditable safety sensors.
VESU learns what each camera sees, monitors whether the view is usable, detects safety-relevant roadway conditions, and publishes evidence-backed incidents into the systems TMC operators already use.
Traffic cameras were built for human observation, not continuous machine understanding.
A model that works on a clean daytime frame can fail when the operator pans the camera, rain hits the lens, glare washes out a lane, or the important event is something the original label set never anticipated.
Different vendors, resolutions, codecs, angles. Reliability varies. A model that works on one feed can fail on the next.
Operators pan the camera. Wind shifts a pole. The learned scene no longer matches what the camera is seeing.
Rain, fog, headlight bloom, low-light compression. The signal degrades long before the feed fails.
Debris, smoke, ponding, pedestrians, work zones, unknown anomalies. The label set will never anticipate everything.
A noisy detector teaches a TMC to ignore it. Precision matters as much as recall.
A frozen, blocked, or mis-aimed camera is not a quiet road. It is a blind spot — and it must be reported.
VESU turns a camera feed into structured roadway telemetry.
First it learns the camera's scene. Then it monitors continuously, checks whether the view is usable, reads sampled frames into structured observations, reasons over time, verifies credible candidates with clips, and publishes incidents with evidence.
Give VESU an approved feed and location context. The system learns the scene instead of asking the customer to annotate every camera.
No new roadside cameras. No mandatory edge box. VESU consumes the feeds the agency already operates and publishes into the systems the TMC already uses.
Every alert carries its clip or keyframes, plain-English explanation, coverage state, and provenance. Defensible months later.
A frozen, obstructed, or mis-aimed camera is not a quiet road. The coverage state is reported as a first-class output.
From an existing feed to a published incident.
Low-cost deterministic checks run constantly. Frame perception is high-recall. Temporal reasoning waits for evidence. Strong verification is reserved for credible candidates. Coverage gaps are output events, not silence.
The overview is a map. Each technical page picks up where this one stops.
How VESU learns the scene from an existing feed — regions, flow, lighting, confounders.
How observations become verified incidents through health, perception, reasoning, and verification.
What VESU monitors and how it reports degraded or missing coverage.
What an incident carries and how it can be replayed.
How strategies improve without silent regressions.
How incidents reach the operational systems agencies already use.
An incident is an evidence package, not a label.
The observed condition, the region, the clip or keyframes, the explanation, the coverage state, and the versioned context that produced it — all in one record that operators can defend later.
"White sedan stationary on the right shoulder for 50 seconds. Traffic flowing in all lanes. View is clear; camera healthy."
Start with the cameras you already have.
VESU is built for agencies that need more from their camera networks without asking operators to watch more screens. Bring the feed environment, the operational workflow, and the safety priorities; we will show how the cameras can become evidence-backed sensors.