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Smart City Video Analytics — Urban AI Monitoring Across Districts

One analytics layer for city-wide CCTV: crowd and flow monitoring at squares and transit hubs, vehicle and incident detection on corridors, and district-level event management in a single platform.

What the AI Monitors

Crowd Detection and People Counting

Density monitoring for squares, stations and events with threshold alerts for overcrowding.

Vehicle and Traffic Detection

Corridor-level vehicle detection, counting and stopped-vehicle alerts for traffic rooms.

Incident and Perimeter Alerts

Intrusion, loitering, abandoned-object style alerts and fire & smoke detection citywide from one rule set.

Multi-Site Event Management

A central AI server aggregates events from district edge boxes with event history search across the city.

Open Integration

Events exposed via HTTP/MQTT/API to city command platforms, GIS and dashboards.

Sovereign, On-Premise Data

All video and analytics stay on municipal infrastructure — no cloud dependency.

Frequently Asked Questions

How does it scale from a pilot to the whole city?

Start with edge AI boxes on selected intersections and squares, then add rackmount AI servers per district — the platform cascades sites into one event search and management view.

Which video management systems does it work with?

It runs alongside existing city VMS platforms via ONVIF/RTSP ingestion and exposes events through standard APIs, so no VMS replacement is needed.

Smart City Video Analytics on Existing Urban CCTV

ADHOOPU smart city video analytics combines crowd detection, people counting, vehicle detection and citywide incident alerts on existing cameras — aggregated into one multi-site management platform, fully on-premise.

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ADHOOPU

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