Edge AI safety glossary.

• Edge AI safety

  Local computer vision that detects physical safety events close to the camera stream.

• CCTV analytics

  Software that reads existing camera feeds and turns video into searchable safety events.

• Near-miss detection

  Detection and indexing of close calls before they become reportable incidents.

• PPE detection

  Computer vision that checks required safety gear in defined camera zones.

• RTSP

  A common streaming protocol used by IP cameras and video systems.

• ONVIF

  A standards-based way for video systems and cameras to interoperate.

• VMS integration

  Routing safety events into the video management system operators already use.

• Safety intelligence

  Structured, timely safety context generated from operational signals.

• Privacy-first video analytics

  Video analytics designed around local processing and limited data movement.

• Edge inference

  Running AI model decisions on local hardware near the camera feed.

• Camera-to-alert latency

  The time from an event appearing in video to a usable alert reaching the response path.

• Forklift-pedestrian detection

  Detection of vehicle and pedestrian paths that may converge in a warehouse or yard.

• Fall detection from CCTV

  Using approved camera views to detect collapse or person-down events.

• Track trespass detection

  Detection of a person or object inside a mapped rail envelope.

• Platform edge safety

  Monitoring the boundary between safe passenger space and the train path.

• Visual smoke detection

  Using existing cameras to detect visible smoke or flame cues in approved zones.

• Crowd density analytics

  Estimating crowd level, movement, and choke points from camera views.

• Restricted-zone intrusion

  Detection of people or vehicles entering a mapped controlled area.

• VMS motion detection

  Rule-based pixel-change detection built into many video management systems.

• Alarm fatigue

  Operator distrust caused by too many low-value or false alarms.

• Edge node

  Local compute hardware that runs model inference beside the camera network.

• Event metadata

  Structured information attached to a detected safety event.

• Pilot KPI

  A measurable criterion used to decide whether a safety AI pilot worked.

• On-premise video analytics

  Video analysis that runs inside the customer environment.

• Camera estate

  The full set of cameras, streams, placements, and VMS connections at a site.

• Operator workflow

  The path an alert follows from detection to human response.

• VMS alarm routing

  Mapping AI events into VMS alarms, priorities, maps, or review queues.

• False-positive review

  A structured way to classify and reduce nuisance safety alerts.

• Existing CCTV readiness

  How suitable a current camera estate is for a safety AI pilot.

• Safety AI use-case count

  The number of incident classes a platform claims to support.

• AI CCTV safety platform

  A platform that adds safety detection to existing camera systems.

• Deployment timeline

  The sequence of steps from camera review to pilot decision.

• AI crawler

  A bot that fetches public web content for AI search, retrieval, or model-facing indexes.

Continue exploring.

• GuideHow to evaluate edge AI safety platformsUse a buyer checklist to turn these definitions into a vendor review.
• PlatformPlatform architectureSee how edge inference, VMS routing, and event metadata work together.
• FAQFAQGet direct answers on latency, camera support, privacy, pricing, and pilots.