Omnicog
Technology
Production Computer Vision requires more than a model that works in a demo. The solution must deal with different cameras, changing lighting, occlusions, distance, connectivity, integration and continuous-operation requirements.
Architecture driven by the scenario
Omnicog can run inference at the edge, on central infrastructure, in the cloud or in hybrid models. The choice depends on latency, camera volume, network availability, security, cost and integration flow.
| Layer | Role |
|---|---|
| Cameras / VMS | Provide the images and streams used by the application. |
| Inference | Models detect, classify, track objects and evaluate rules. |
| Events | Results are consolidated into data, alerts, evidence and indicators. |
| Integration | APIs, VMS, messaging, dashboards and systems receive the generated context. |
Edge AI
Processing close to cameras can reduce latency, save bandwidth and keep analysis local when connectivity is limited. Architecture is defined according to the project, not as a fixed rule.
Computer Vision models
Applications can combine detection, classification, tracking, zone analysis, counting, dwell time, trajectories and temporal rules. The set depends on what the operation needs to see.
Integration by design
An isolated detection rarely creates value. The event must reach the system or person able to act, so integration is treated as part of the solution from the beginning.
Privacy, security and governance
Purpose, access, retention and data minimization should be part of application design and follow applicable rules and customer policies.
How we validate
- Select representative scenes from the real environment.
- Define classes, events and success criteria.
- Run the pilot and measure field results.
- Adjust models, rules, cameras or architecture as needed.
- Integrate and scale only when the application demonstrates value.
Want to discuss architecture, cameras or integration? Talk to our technical team.