Smart city intelligence converts municipal camera networks into continuous measurements of people, vehicles, cleanliness and infrastructure condition, so decisions are made on evidence.
Municipal operations depend on knowing how public space is actually used: how many people pass through a plaza, where traffic congests and when, which waste points overflow, where road surfaces or street furniture need attention. Today this knowledge comes from periodic surveys, complaints and manual patrols.
Cities already operate large camera networks for safety and traffic, yet these feeds are rarely used for measurement. The result is reactive management: problems are addressed after residents report them, and planning decisions rest on sparse, outdated samples.
Our smart city solution turns those camera networks into a continuous sensing layer. Counting, classification and condition monitoring run at the edge, aggregate across the city and feed dashboards and open data interfaces that departments can act on.
Each capability is marked so you can distinguish what we deliver today from capabilities engineered to your specification.
Directional counts at entrances, crossings and corridors from tracked trajectories, aggregated by time of day and location.
Detection of overflowing bins, illegal dumping and litter accumulation at monitored waste points, with alerts to collection teams.
Detection of obstructions, stopped vehicles, flooding and abnormal conditions on monitored road segments.
Vehicle counting and classification by lane and direction, queue length estimation and congestion state on arterials and junctions.
Occupancy, dwell and flow patterns in parks, plazas and transit areas to inform operations, events and planning.
Visual condition checks of monitored assets such as signage, lighting and barriers, flagging damage or absence.
City-wide dashboards, departmental views and standards-based data feeds for planning teams and third parties.
Counting and flow measurement using anonymized detections, with no identity data retained, configurable to local regulation.
The pipeline behind the solution, stage by stage.
Existing traffic, safety and facility cameras are connected to edge nodes placed in cabinets or data rooms close to the feeds.
Detection, tracking and classification run locally. Only counts, events and anonymized metadata leave the edge, keeping bandwidth and privacy exposure low.
A central service collects measurements from every node into time-series and event stores, normalized by location and asset.
Traffic, sanitation, parks and infrastructure teams see tailored dashboards and receive alerts relevant to their operations.
Historical patterns feed studies, event planning and investment decisions with continuous rather than sampled data.
Operational teams respond to conditions as they develop rather than after complaints.
Planning decisions draw on continuous measurements of how spaces are actually used.
Waste collection, maintenance and traffic management are prioritized by observed need.
A single camera network serves multiple departments.
Privacy obligations are met by processing locally and retaining only aggregate data.
Real-time video understanding, event detection, behavioral analysis and multi-camera analytics.
Deploy optimized AI models directly on edge hardware for responsive local inference.
Develop custom machine learning pipelines, predictive systems and intelligent data solutions.
Tell us which questions your departments cannot answer today. We will show how existing cameras can start answering them.