Guide
What to Evaluate Before Piloting AI on Existing CCTV Cameras
- Author
- DHI EngineeringEdge AI Architecture
- Published
- 2026-10-06
- Read time
- 6 min read
- Updated
- 2026-10-06
What should a warehouse safety team evaluate before piloting AI on existing CCTV cameras?
Settle five things first: which cameras and streams the software reads, how its events reach your VMS and response path, where raw video goes, how camera-to-alert time is validated, and what one zone and one workflow the pilot covers. The pilot should define KPIs, camera scope, alert routing, review cadence, and success criteria before deployment.
Which cameras and streams does it read?
DHI supports RTSP and ONVIF-compatible cameras. Confirm that each camera in the zone exposes a stream the edge node can read, since DHI reads RTSP directly and runs alongside the VMS you already have without replacing it.
How do events reach your VMS and response path?
DHI provides REST APIs, webhooks, VMS integrations, and event-routing patterns for common incident management workflows. Routing DHI events into a specific VMS alarm workflow is scoped integration work rather than a shipped connector, so decide who owns that work and where the alert should land.
Where does raw video go?
Raw video stays on your premises by default. Only structured safety events or policy-approved clips are transmitted off-site when your workflow allows it.
How is camera-to-alert time validated?
Camera to alert is longer than inference: detectors sample each camera every 1.5 seconds by default, and that sampling interval, not inference, is the dominant term. DHI does not offer a latency guarantee, so validate camera-to-alert on the actual network, VMS workflow, and alert destination.
What does each event type need?
Forklift conflict, falls, smoke, crowding, track trespass, and restricted-zone events each have different tuning and validation work. Pick the one event type that matters most for the zone.
What should the pilot scope be?
A Focused Pilot is fixed-price for 30 days, one workflow and one zone, using your current cameras, video management system and response workflow. It includes an edge-node placement review and a VMS or local alert-routing plan.
What evidence does DHI have to offer?
DHI has no customer results to share, publishes no accuracy or false-alarm percentages, and makes no certification or compliance claims. Clips and screens on this site are test footage and demo data, so the pilot on your own cameras is where the evidence comes from.
Continue exploring.
- GuideEdge AI Safety Platform Evaluation GuideThe full buyer guide for comparing platforms.
- GuideIndustrial AI Pilot ChecklistA checklist for turning evaluation into a pilot.
- ChecklistForklift Pedestrian Safety ChecklistA practical checklist for a forklift zone.
- PricingPricing and pilot scopeSee what changes pilot and rollout scope.
Validate "what to evaluate before piloting ai on existing cctv cameras" on your live feeds.
Coordinate a 30-day architecture review and live camera validation based on the protocol described in "what to evaluate before piloting ai on existing cctv cameras" for your facility.
Best follow-up: bring the current workflow that "What to Evaluate Before Piloting AI on Existing CCTV Cameras" is supposed to improve.
- Book a demoSee the flow on a real operating scenario and scope a pilot around one facility or corridor.
- See deployment architectureReview camera ingest, edge inference, alert routing, and what stays on-premises.
- Get the implementation checklistDownload the deployment checklist buyers use before green-lighting an industrial AI pilot.
- Talk to an engineerBring camera count, VMS constraints, latency expectations, and privacy requirements to a technical review.