REF-02Reference scenarioLogistics & Warehousing
Intelligent video platform for a warehouse campus
A reference CCTV/VMS design for 120 IP cameras, resilient recording and operator-led AI analytics.
Published
This is a planning example written by EngineSpire to show how we would approach the problem. It is not a completed client project, and its outcomes are design targets rather than measured results.
The challenge
Security teams need reliable coverage across loading bays, perimeters and high-value storage while operations need faster retrieval of relevant video. The existing recorder-by-recorder workflow makes incidents slow to reconstruct.
The solution pattern
The reference design centralizes cameras in a scalable VMS, separates camera traffic, provides redundant recording capacity sized from measured bitrates and applies analytics only to defined scenes. Health monitoring covers camera loss, storage and time synchronization, while role-based access protects evidence.
How the work would be phased
- PHASE 01
Define each view's purpose
Agree for every camera whether it must detect, observe, recognize or identify, and record lighting, mounting height and the retention each area needs.
- PHASE 02
Measure real bitrates
Record representative cameras by day, by night and during busy periods, then size recording storage from those measurements plus a capacity margin.
- PHASE 03
Design the platform
Select the VMS against written criteria, isolate camera traffic, add recording redundancy, synchronize time across devices and define operator roles.
- PHASE 04
Deploy by zone
Install and commission one zone at a time, checking each image against its stated purpose and enabling health alerts before moving on.
- PHASE 05
Introduce analytics carefully
Enable analytics on selected scenes only, review false alarms with operators and extend coverage when the results are useful.
Trade-offs and considerations
- Retention drives storage cost more than camera count does. Set retention by area and purpose instead of one number for the whole campus.
- Night scenes can need far more bitrate than daytime scenes because of sensor noise, so sizing from daytime samples underestimates storage.
- Analytics accuracy depends on lighting, camera angle and scene complexity. Pilot on real scenes before licensing every camera.
- Video of identifiable people is personal information. Document the purpose, access rules and retention, following the Data Privacy Act and National Privacy Commission guidance.