Track Trespass Detection.
Stop trains before tragedies occur. DHI differentiates between authorized maintenance crews and civilian trespassers, alarming dispatch the second an unauthorized body enters the rail envelope.
Logic Validation
Track Trespass Detection requires precise computer vision engineering.
Verify the specific neural thresholds and VMS integration triggers used to automate this safety protocol.
Edge Processing
Stop trains before tragedies occur. DHI differentiates between authorized maintenance crews and civilian trespassers, alarming dispatch the second an unauthorized body enters the rail envelope.
Inferencing
Authorized vs Unauthorized: The model reads high-visibility PPE to distinguish authorized hi-vis maintenance crews from civilian trespassers, so routine track work does not trigger alerts but an unprotected body in the rail envelope does.
Integration fit
Bypass the VMS entirely if needed. DHI can trigger physical sirens, flashers, or route IP pages directly to the train control center the moment track occupancy is compromised.
Failure mode coverage
Motion Path Analysis: Directional human travel toward the rails is separated from ambient movement like blowing debris or animals, keeping nuisance alarms low.
Use-case fit
Low-Light Augmentation: Inference runs directly on IR and thermal camera feeds, so detection holds up in unlit tunnels and overnight yards.
Direct answer
What makes track trespass detection different from ordinary motion detection?
Track trespass detection has to understand the mapped rail envelope. DHI is built to confirm a human body inside the restricted area, distinguish likely authorized activity from unauthorized intrusion when the scene supports it, and route the event to dispatch or local warning systems quickly.
Rail-envelope fit
The first setup should map the trackbed, fence line, bridge approach, tunnel portal, or yard edge so the model knows the actual danger boundary.
Nuisance review
The pilot should separate wildlife, wind, debris, passing trains, and authorized maintenance from confirmed human occupancy.
Dispatch fit
The useful event includes camera, zone, time, direction, and urgency context so control can warn, slow, dispatch, or escalate.
Edge fit
Corridor cameras can have constrained backhaul, so local inference reduces dependence on a cloud path for the first alert.
Pilot assumptions to validate
- The pilot zone has a clear mapped rail envelope and enough camera coverage to confirm human occupancy.
- Authorized work crews, maintenance windows, and PPE rules are documented before testing.
- Dispatch or field response teams agree how each alert type should be handled.
Rail corridor fit
Trespass detection has to work where bandwidth and visibility are both constrained.
Rail trespass is different from station platform safety because the camera may sit on a pole, in a tunnel mouth, or along a yard fence with limited backhaul. The page is tuned around corridor occupancy and dispatcher escalation.
Primary scene
Ideal pilot zones include known fence breaches, bridge approaches, tunnel portals, and yard edges where a person can enter the rail envelope without passing through a staffed station.
Operator action
The useful alert is not just motion. It is a confirmed human body inside a mapped track envelope, with location metadata precise enough for dispatch to slow movement, trigger a field response, or warn operators.
Pilot measure
A rail pilot should track nuisance alerts from wildlife, wind, work crews, and passing trains separately from confirmed human intrusion so the team can prove the model is reducing noise, not adding it.
Detection Logic
- Authorized vs Unauthorized: The model reads high-visibility PPE to distinguish authorized hi-vis maintenance crews from civilian trespassers, so routine track work does not trigger alerts but an unprotected body in the rail envelope does.
- Motion Path Analysis: Directional human travel toward the rails is separated from ambient movement like blowing debris or animals, keeping nuisance alarms low.
- Low-Light Augmentation: Inference runs directly on IR and thermal camera feeds, so detection holds up in unlit tunnels and overnight yards.
Automated Dispatch Paging
Bypass the VMS entirely if needed. DHI can trigger physical sirens, flashers, or route IP pages directly to the train control center the moment track occupancy is compromised.
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
Rail infrastructure AI solutions.
Platform Edge
Catch falls before they reach the tracks.
Latency Kills
Why cloud analytics are too slow for fast trains.
Rail Trespass Case Study
How edge detection on the pole keeps dispatch in the loop.
Crowd Monitoring
Reading platform density before crowds reach the edge.
Deploy track trespass detection as a Pilot.
Coordinate a 30-day architecture review and live camera validation for track trespass detection 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.