Forklift-Pedestrian Collision Prevention.
Loading docks and cross-aisles are blind spots. DHI maps the speed and direction of heavy machinery and walking personnel at the same time, flagging the moment two paths are set to intersect before they make contact.
Logic Validation
Forklift-Pedestrian Collision Prevention requires precise computer vision engineering.
Verify the specific neural thresholds and VMS integration triggers used to automate this safety protocol.
VMS Alerting
Loading docks and cross-aisles are blind spots. DHI maps the speed and direction of heavy machinery and walking personnel at the same time, flagging the moment two paths are set to intersect before they make contact.
Video Processing
Multi-Object Trajectory: The model continuously tracks the speed and heading of every forklift and reads it against the walking paths of nearby pedestrians, so a converging course is flagged while there is still time to react.
Integration fit
DHI writes a structured alarm event straight into Genetec Security Center and Milestone XProtect, surfacing the live forklift feed on the operator's monitor. The same trigger can flash LED cross-aisle signs or sound a localized siren over connected IoT hardware the instant a forklift enters a walking zone too quickly.
Failure mode coverage
Blind-Corner Prediction: Intersecting camera views let the system see a pedestrian waiting behind racking that the driver cannot, and warn the operator before the forklift clears the shelving.
Use-case fit
Near-Miss Indexing: Close calls that end without injury are automatically clipped and saved, giving safety teams a concrete library of incidents to review and use in driver retraining.
Direct answer
Can forklift-pedestrian collision prevention work with existing CCTV?
DHI's Edge AI prevents forklift-pedestrian collisions by analyzing real-time video feeds on-premises. Operating at under 150ms latency, it tracks vehicle trajectory and pedestrian walk paths simultaneously, triggering immediate VMS alerts or local sirens before impact. This system does not require cloud streaming, ensuring raw video never leaves the facility.
Trajectory fit
A forklift alert is useful only when the system sees both the vehicle path and the pedestrian path early enough to warn before they intersect.
Blind-corner fit
The highest-value cameras are cross-aisle, rack-end, dock, and staging views where a driver cannot see the full pedestrian approach.
Near-miss proof
The pilot should produce clips and counts for close calls, repeat zones, warning time, and operator acknowledgement.
Workflow path
Alerts should route to a local signal, VMS alarm, supervisor device, or other channel that can change behavior in the aisle.
Pilot assumptions to validate
- The camera view includes enough floor geometry to map both vehicle and pedestrian motion.
- The site chooses one first response, such as a light stack, horn, VMS alarm, or supervisor alert.
- Near-miss definitions are agreed before the pilot so the review is consistent.
Sources and next pages
Warehouse fit
Forklift safety is a path-intersection problem, not a generic person detector.
This use case is built around vehicle momentum, rack occlusion, pedestrian walk paths, and the few seconds where a warning can still change the outcome before impact.
Primary scene
High-value camera locations include cross-aisles, dock doors, battery charging areas, pallet staging lanes, and blind corners where racking blocks the driver's view.
Operator action
The alert should reach the person who can intervene fastest: a local light stack, horn, supervisor tablet, or VMS alarm tied to the exact aisle where the convergence is happening.
Pilot measure
Measure near-miss clips, time-to-warning, forklift speed at the conflict point, and whether repeat hot spots emerge by shift or aisle. Those records turn anecdotal safety concerns into a retraining plan.
Detection Logic
- Multi-Object Trajectory: The model continuously tracks the speed and heading of every forklift and reads it against the walking paths of nearby pedestrians, so a converging course is flagged while there is still time to react.
- Blind-Corner Prediction: Intersecting camera views let the system see a pedestrian waiting behind racking that the driver cannot, and warn the operator before the forklift clears the shelving.
- Near-Miss Indexing: Close calls that end without injury are automatically clipped and saved, giving safety teams a concrete library of incidents to review and use in driver retraining.
Active Horns & Signals
DHI writes a structured alarm event straight into Genetec Security Center and Milestone XProtect, surfacing the live forklift feed on the operator's monitor. The same trigger can flash LED cross-aisle signs or sound a localized siren over connected IoT hardware the instant a forklift enters a walking zone too quickly.
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
Warehouse Safety AI
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Fall Detection
When collisions happen, dispatch EMTs.
Fire Hazards
Protecting high-density storage from thermal events.
Edge vs Cloud AI
Why blind-corner alerts cannot survive a cloud round-trip.
Forklift-Pedestrian Safety Checklist
A practical checklist for evaluating camera AI in forklift conflict zones.
Warehouse Near-Miss Detection Guide
How to turn close calls into searchable safety records by zone and shift.
Deploy forklift-pedestrian collision prevention as a Pilot.
Coordinate a 30-day architecture review and live camera validation for forklift-pedestrian collision prevention 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.