Edge AI or cloud: where should computer vision run?

Published on by Omnicog team. Updated on .

There is no single right architecture for every case. Processing video at the edge, on central servers or in the cloud involves trade-offs in latency, bandwidth, security, cost and availability. Many operations combine analysis close to the cameras with a central layer for events and history.

When edge AI makes sense

  • Low latency is important.
  • The connection is limited or unstable.
  • The volume of video makes sending it to the cloud impractical.
  • The operation must keep running without an external connection.

When central or cloud processing makes sense

  • Sites have good connectivity.
  • Centralizing management and processing simplifies operations.
  • The project needs to scale resources up and down quickly.

Hybrid models

Many operations combine analysis close to the cameras with a central layer for events, dashboards, history and integrations. This is the case of the highways served by Omnivias: the local unit keeps working even without the central system.

Read next: Omnicog’s technology

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.

How the assessment works

Frequently asked questions

Does video analytics work without internet?

Yes, when it runs at the edge. The local device analyzes the cameras and stores the events, which are sent to the central system once the connection is back.

What do you want your cameras to tell you?

Tell us what you need to detect, measure or monitor. We start with the problem, not the technology, and the assessment uses images from the cameras you already have.

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