Platform Edge Safety.
Automated person-down alerts at the exact platform edge. DHI monitors tactile warning strips and platform boundaries in real time, alerting station managers the instant a commuter collapses or stands dangerously close to moving trains.
Logic Validation
Platform Edge Safety requires precise computer vision engineering.
Verify the specific neural thresholds and VMS integration triggers used to automate this safety protocol.
Alert Latency
Automated person-down alerts at the exact platform edge. DHI monitors tactile warning strips and platform boundaries in real time, alerting station managers the instant a commuter collapses or stands dangerously close to moving trains.
Anonymized
Dynamic Virtual Tripwires: AI draws a persistent boundary over the platform's yellow tactile warning strip, triggering automated alerts for any body crossing into the danger zone.
Integration fit
When a commuter crosses the danger threshold, DHI automatically flags the event in your VMS alarms and streams the associated camera feed to the operator's main monitor instantaneously.
Failure mode coverage
Skeletal Posture Mapping: Detects if a body is upright, leaning dangerously, or has fallen horizontally near the tracks, the exact 'person-down' scenario transit agencies need solved.
Use-case fit
Crowd Push Estimation: Analyzes crowd density gradients to predict involuntarily pushed commuters.
Direct answer
Can AI monitor a transit platform edge with existing station cameras?
Yes, when the camera view clearly sees the tactile strip, waiting zone, and edge boundary. DHI maps the platform danger zone locally and routes edge encroachment, falls, and person-down events into station operations fast enough for a response before the train arrives.
Boundary fit
The model should be tuned to the actual yellow line, tactile strip, track edge, and station geometry rather than a generic motion zone.
Response fit
A useful event includes platform, camera, zone, posture, and urgency so staff know whether to dispatch, warn, or escalate.
Crowd context
Edge risk often rises when dwell time, crowding, or service disruption pushes passengers closer to the boundary.
Pilot proof
The pilot should measure edge encroachment, person-down alerts, nuisance triggers, and operator acknowledgement during real service periods.
Pilot assumptions to validate
- The camera angle sees the edge clearly and is not blocked by columns, signage, or train movement.
- Station staff agree which events require immediate interruption of the normal control-room workflow.
- The pilot is evaluated during normal service conditions, not only during an empty-platform test.
Transit platform fit
Platform-edge monitoring is about seconds before train arrival, not after-the-fact review.
This page focuses on the yellow-line boundary and person-down scenarios that happen on a live platform, where the correct response is an immediate station action rather than an investigation later in the VMS archive.
Primary scene
Best fit is an island or side platform where existing cameras see the tactile strip, the waiting zone, and the first feet of the track bed. The model can then separate normal waiting behavior from leaning, collapse, or a body crossing the edge.
Operator action
Alerts should surface to the station control room with the nearest camera, a platform-zone label, and a priority high enough to interrupt routine monitoring before a train enters the block.
Pilot measure
A useful pilot measures how often DHI flags edge encroachment, how quickly station staff acknowledges it, and whether warnings correlate with crowding, dwell time, or specific platform segments.
Detection Logic
- Dynamic Virtual Tripwires: AI draws a persistent boundary over the platform's yellow tactile warning strip, triggering automated alerts for any body crossing into the danger zone.
- Skeletal Posture Mapping: Detects if a body is upright, leaning dangerously, or has fallen horizontally near the tracks, the exact 'person-down' scenario transit agencies need solved.
- Crowd Push Estimation: Analyzes crowd density gradients to predict involuntarily pushed commuters.
Genetec & Milestone Native
When a commuter crosses the danger threshold, DHI automatically flags the event in your VMS alarms and streams the associated camera feed to the operator's main monitor instantaneously.
Industrial Privacy & Sovereignty
DHI's models do not stream raw video to the public cloud. All safety inferencing occurs on-premise within your local network, supporting privacy review and tighter control for critical infrastructure environments.
Continue Exploring
Transit Safety Hub
Back to the public transport intelligence overview.
Track Trespass
What happens when they fall onto the rails.
Platform Crowding
Predicting crushes before people are pushed.
Rail Trespass Case Study
Edge detection at the trackside, validated in the field.
Deploy platform edge safety as a Pilot.
Coordinate a 30-day architecture review and live camera validation for platform edge safety in your facility.
Compatible with existing Milestone, Genetec, and standard ONVIF streams.
Request a demo
See the flow on a real operating scenario and scope a pilot around one facility or corridor.
See deployment architecture
Review camera ingest, edge inference, alert routing, and what stays on-premises.
Get the implementation checklist
Download the deployment checklist buyers use before green-lighting an industrial AI pilot.
Talk to an engineer
Bring camera count, VMS constraints, latency expectations, and privacy requirements to a technical review.