RoadMonitor is the AI intelligence layer that connects to your existing road camera network — fixed, mobile or vehicle-mounted — and detects distracted driving, speeding, seatbelt and plate offences in real time, predicts emerging safety risks, and feeds verified evidence straight into your infringement workflow.
RoadMonitor doesn't replace your infrastructure — it makes it intelligent. The platform ingests feeds from the cameras you already operate and runs a four-stage pipeline built for evidentiary standards, not just analytics dashboards.
Fixed gantries, trailer units, vehicle-mounted rigs, CCTV and ANPR networks — connected over standard streaming protocols, with edge processing where bandwidth is limited.
Vision models detect handheld phone use, seatbelt non-compliance, speed against posted limits, plates, vehicle class and hazardous behaviour — simultaneously, on every lane.
Only candidate offences reach a trained reviewer, anonymised until confirmed. Everything else is discarded automatically. No offence, no image, no record.
Verified violation packages — imagery, plate, time, location, calibrated speed data — flow into your existing infringement issuance system via secure, auditable APIs.
Speeding, distraction, seatbelts, impairment and fatigue — the "fatal five" — drive the majority of road deaths. RoadMonitor targets the ones a camera can see, and flags patterns that suggest the ones it can't.
Dual-angle analysis identifies handheld devices at the ear, in the lap or in hand — day or night, at highway speeds, through windscreen glare.
Point speed via radar/vision fusion plus ANPR-based average-speed enforcement across corridors — calibrated to evidentiary standards.
High-accuracy ANPR for infringement matching, unregistered and hotlist vehicle alerts, and corridor journey-time analytics.
Driver and front-passenger restraint detection through the windscreen, using infrared capture for reliable all-hours coverage.
Stopped vehicles, debris, pedestrians in live lanes, wrong-way drivers and queue formation — surfaced to traffic operations in seconds.
Structured violation packages delivered into your existing fine-issuance and prosecution systems, with full chain-of-custody audit logs.
Every detection is also a data point. RoadMonitor aggregates behaviour across your network to build a live risk model of the road itself — so you can intervene with engineering, signage or patrols before the statistics do it for you.
Automated enforcement lives or dies on legitimacy. RoadMonitor is engineered so that every fine can withstand a courtroom, an audit and a newspaper front page.
Non-offence imagery is deleted automatically at the edge. Reviewers see anonymised images — no plate, no passengers — until an offence is confirmed. Data residency stays in-jurisdiction.
AI proposes; people decide. No infringement is issued on model output alone — every candidate offence passes a trained human review stage, logged and auditable end-to-end.
Calibrated capture, cryptographically sealed violation packages, full chain of custody and transparent model documentation — built for prosecution standards and regulator scrutiny.
Because RoadMonitor is an intelligence layer — not a hardware lock-in — you can start with the cameras in the ground today and add capture points where the risk model says they'll matter most.
Connect current traffic and enforcement cameras via stream ingestion. Fastest path to a live pilot.
Permanent multi-lane enforcement points with infrared capture for round-the-clock coverage.
Free-standing units moved to wherever the risk index — or community concern — points next.
Patrol and survey vehicles become moving detection points across the wider network.
RoadMonitor runs on any compliant capture hardware — including units you already own. These are the three most common deployment classes across our pilot corridors.
A RoadMonitor pilot connects to a single high-risk corridor on your existing cameras, runs detection in shadow mode alongside your current process, and reports verified results — offences detected, risk mapped, workload saved — before you commit to anything.