Applications and field experience

Computer vision applications need to be assessed in the real environment. Here we show how Omnicog’s technology is used, without presenting conceptual scenarios as client cases that don’t exist.

Highways: technology at scale with Omnivias

Omnicog’s experience with computer vision in infrastructure gave rise to Omnivias, dedicated to highway concessionaires. It runs on more than 1,000 cameras in production with traffic analysis and automatic incident detection. At the Rota de Santa Maria concessionaire, a client that authorizes being named, more than 400 cameras run SAT and DAI, with two years of operation.

It is the main public example of the technology operating at scale: cameras in production, distributed architecture and AI integrated into highway operations.

Ports: class detection in an operational environment

Omnicog has already applied detection of people, vehicles and equipment in port environments, providing more visibility over access points, yards and work zones.

People flow

Counting, occupancy, dwell time and trajectories in airports, hospitals, buildings and public spaces show how the environment is actually used, not just the recorded video.

Mining and industry

In large operations, cameras monitor zones, people, vehicles and machinery. Feasibility is assessed with real footage, because dust, distance, weather and light affect the result.

What we consider a useful case

A useful case makes the problem, architecture, metrics and change in operations clear. When the client authorizes it, we show names and numbers; when they don’t, we describe the capability without inventing results.

Assessment of your cameras

The first step is to look at the cameras you already have and tell you what they can do. Then a proof of concept with your own footage shows the result in the field. Depending on the size of the project, both steps can be free of charge, and the proof of concept starts running within 10 days of the agreement between the parties.

Have a similar scenario?

Assess it with our team using footage from your own cameras.

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