Planning AI Video Analytics Architecture for 50–100 Cameras

Analysing one camera is easy. At 50–100 cameras, architecture decisions determine cost, reliability and how much maintenance the system will need.

1. Camera inventory and prioritisation

List each camera’s location, make/model, resolution, stream access and field of view, then map which analytics it needs. Not every camera needs to be analysed — start with the views that cover the highest risk or value.

2. Stream and frame-rate selection

Analytics rarely needs the full recording frame rate. A few frames per second can be enough for zone monitoring; fast vehicles need more. Getting this right has a large effect on hardware requirements.

3. Hardware sizing

• Load ≈ cameras × frames per second per analytic × model cost.
• Several edge devices distributed by area reduce network load and single points of failure.
• Leave headroom for additional use cases.

4. Network and security

Keep cameras and analytics devices on a dedicated VLAN, separate from office networks and the internet. Restrict outbound traffic to event data and dashboard access.

5. Events and storage

Raw video is already recorded by the NVR/VMS. The analytics layer stores events, metadata and short evidence clips, with retention and access aligned to your data-protection policy.

6. Health monitoring

Monitor stream loss, view changes (a camera knocked out of position or dirty lens) and device health — otherwise a system can go silently blind. More context: CCTV AI analytics.