Smart City Traffic Management and Surveillance

Citywide deployment of 200+ AI-powered surveillance and traffic analysis nodes, processing 800+ camera feeds in real-time for traffic optimization and public safety.

The Challenge

A city of 2.5 million residents needed to modernize their traffic management and public safety infrastructure. Legacy systems couldn't handle the volume of data from 800+ traffic and surveillance cameras. Cloud-based analytics proved too expensive due to bandwidth costs and introduced unacceptable latency for traffic signal optimization. The city required a distributed edge computing solution that could operate reliably in outdoor cabinets with extreme temperature variations and limited maintenance access.

The Solution

We designed a distributed edge AI architecture using 200 NVR-9000 platforms strategically placed across the city. Each node processes 4-8 camera feeds locally, performing vehicle counting, classification, license plate recognition, and incident detection in real-time. The rugged, fanless design enables installation in roadside cabinets without climate control. Intelligent power systems with battery backup ensure continuous operation during grid outages. A central command center aggregates insights from all nodes for city-wide traffic optimization and emergency response coordination.