Sitemap
Every page, in one place
118 pages across 7 clusters. Browse the whole site, including solutions, use cases, comparisons, guides, and the blog, without hunting through the nav.
Core
12 routesTop-level pages.
Edge-Native Safety IntelligenceTurn existing CCTV cameras into real-time safety sensors. Detect falls, collisions, and hazards at the edge with under 150ms latency.Edge-Native AI Platform ArchitectureAnalyze video on-premises with NVIDIA Jetson hardware. Under 150ms inference, zero raw video leaving your infrastructure.Enterprise Security & Privacy PosturePrivacy-first safety AI. Raw video never leaves your site. Encryption, policy-based filtering, and secure audit trails.Pilot-First Pricing & DeploymentLow-risk 4-week pilot deployments with defined KPIs and measurable results for your facility.Our Mission: The Safety Intelligence LayerBuilding the safety intelligence layer for the physical world by turning CCTV into real-time sensors for proactive response.Expert Leadership in Edge AI & OpsField operations meets AI engineering. The team building real-time, privacy-preserving safety intelligence.DHI DemoReview camera fit, operator workflow, and pilot scope for edge AI safety detection.DHI Partner and Integrator ProgramPartner with DHI to bring safety detection to existing CCTV, VMS, and industrial camera environments.DHI Press and MediaApproved company facts, story angles, and media contact details for DHI Technologies, Inc.Edge AI Safety FAQRTSP/ONVIF compatibility, sub-150ms edge latency, and privacy-first VMS integration (Genetec, Milestone).Book a Pilot ConversationDiscuss a safety audit or pilot deployment. Start defining safety KPIs and bringing AI detection to your site.Sitemap: Every Page on DHIA complete, browsable index of every page on the DHI site: solutions, use cases, comparisons, technical guides, and the blog.
Solutions
7 routesIndustry vertical hubs.
Safety Solutions for Transit & LogisticsAI-powered detection for track trespass, platform safety, forklift-pedestrian conflicts, and warehouse hazards.Transit & Rail Safety AIEdge AI video analytics for metro stations, rail platforms, and transit infrastructure.Logistics & Warehouse Safety AIPrevent forklift collisions and fire hazards in warehouses and distribution centers.Warehouse Safety AIDetect forklift conflict, near misses, falls, smoke, fire, and restricted-zone events from existing CCTV.Manufacturing Industrial Safety AIAutomate PPE compliance and restricted-zone monitoring using on-premises Edge AI.Healthcare Safety AIHospital and clinic safety analytics for falls, person-down events, restricted areas, and security risk.Government Safety AIPublic building video analytics for restricted access, crowd buildup, and facility safety events.
Use Cases
7 routesPer-incident detection models.
Edge AI Safety Use CasesTargeted AI detection models for track trespass, falls, and forklift-pedestrian safety.Platform Edge Safety MonitoringContinuous AI monitoring of the platform edge with alerts before the train arrives.Track Trespass DetectionDetect unauthorized rail track intrusion in milliseconds and warn dispatchers.Forklift Collision PreventionPredict forklift-pedestrian collisions with cross-aisle trajectory mapping.AI Fall Detection & Person-Down AlertsPassive CCTV fall detection alerts supervisors the moment a worker collapses.Visual AI Smoke & Fire DetectionDetect fires before thermal sensors trigger, using existing cameras.Crowd Density & Surge MonitoringPrevent crushing incidents with real-time crowd density and flow analysis.
Compare
4 routesHead-to-head comparisons against the alternatives.
How DHI ComparesEdge vs cloud, AI vs human guards, and neural detection vs legacy VMS motion with honest trade-offs.Edge AI vs Cloud AILatency, privacy, and cost differences of edge-native vision versus cloud streaming.AI Surveillance vs Human GuardsEdge AI accuracy against the failure rates of manual CCTV monitoring.AI CCTV vs Traditional VMS MotionNeural edge AI versus legacy VMS pixel-shift motion detection.
Resources
41 routesTechnical guides & case studies.
Edge AI Safety Guides & Case StudiesTechnical guides, deployment case studies, and VMS integration checklists.Milestone XProtect AI Integration ChecklistStep-by-step technical configuration for routing DHI edge analytics into Milestone XProtect alarm and Smart Client workflows over ONVIF.Genetec Security Center AI Integration GuideHow to add DHI edge safety analytics to a live Genetec Security Center deployment using ONVIF events and the Security Center alarm engine.Reducing Rail Track Trespass Risk Across 300 Miles of Analog CCTVA deployment deep-dive on running autonomous video intelligence across active rail corridors without fiber backhaul and without replacing analog CCTV hardware.Why Cloud AI Cannot Stop a Forklift in TimeAn architectural review of the physical latency limits of cloud inference versus edge-native processing, and why momentum makes the difference fatal.On-Premise Video Analytics: A Data Privacy and Security GuideHow edge-native video analytics keeps footage on premise, narrows the attack surface, and simplifies the privacy and compliance story for IT and Legal.The Industrial AI Pilot Planning & Implementation ChecklistA technical framework for deploying edge compute on an existing VMS network without disrupting live security operations, with success criteria you can defend.Warehouse Near-Miss Detection GuideHow warehouse teams can use existing CCTV to detect forklift near misses, aisle conflicts, blocked walkways, and repeat safety hot spots.Forklift-Pedestrian Safety AI ChecklistA practical checklist for evaluating forklift-pedestrian collision prevention with camera AI, edge inference, VMS alerts, and measurable pilot criteria.CCTV AI Analytics Guide for Existing Camera EstatesHow to evaluate CCTV AI analytics for existing cameras, including stream access, edge processing, VMS workflow, privacy, and pilot metrics.How to Evaluate Edge AI Safety PlatformsA buyer guide for comparing edge AI safety platforms by latency, camera fit, VMS integration, privacy posture, reliability, and pilot proof.Best Edge AI Workplace Safety Platforms 2026: Evaluation CriteriaA neutral evaluation framework for choosing edge AI workplace safety platforms in 2026 without relying on vendor rankings or unverified claims.Best AI CCTV Safety Platforms for Warehouses: What to EvaluateA warehouse buyer guide for evaluating AI CCTV safety platforms by forklift risk, near-miss capture, fire cues, VMS fit, and operator trust.On-Premise Video Analytics Buying GuideA buying guide for evaluating local video analytics, raw-video control, event routing, and privacy review before deployment.Forklift Safety AI Buyer GuideHow warehouse teams should evaluate forklift-pedestrian detection, blind-corner warning, alert routing, and near-miss review.Genetec AI Safety Integration OptionsA practical overview of ways to route edge AI safety events into Genetec Security Center workflows.Milestone XProtect AI Safety Integration OptionsHow to evaluate AI safety alert routing inside Milestone XProtect without replacing the recording workflow.Edge AI vs Cloud AI for Forklift SafetyCompare local inference and cloud analytics for forklift-pedestrian conflict, blind-corner alerts, and near-miss capture.AI Fall Detection CCTV vs Wearable Fall DetectionCompare camera-based person-down detection and wearable fall detection by coverage, adoption, privacy, and response workflow.Track Trespass Detection Using Existing CCTVHow transit and rail teams can evaluate track trespass detection with current station, depot, and corridor cameras.On-Premise Video Analytics for Privacy-Sensitive FacilitiesA privacy review guide for healthcare, public-sector, and enterprise sites evaluating local video analytics.Why Use-Case Count Is Not Enough in Safety AIWhy buyers should evaluate depth, camera fit, workflow fit, and proof quality instead of choosing by the longest detection list.DHI vs Cloud Workplace Safety AIA neutral architecture comparison between DHI's local inference model and cloud-first workplace safety analytics.Buyer-Stage Comparison Tables for Safety AIComparison tables for early research, technical validation, pilot planning, and enterprise rollout decisions.Where DHI Is Not the Right FitA trust-focused guide to situations where DHI may not be the right choice for a site, workflow, or buying team.Latency Benchmark Template for Edge AI SafetyA template for measuring camera-to-alert latency during an edge AI safety pilot without overstating results.False-Positive Taxonomy for Safety AIA practical taxonomy for classifying nuisance alerts by scene, model, threshold, workflow, and review causes.Existing CCTV Readiness ScorecardA scorecard for deciding whether current cameras, streams, VMS workflows, and response paths can support a safety AI pilot.Edge Hardware Capacity GuideHow to think about edge node sizing, camera count, model count, stream quality, and failover for safety AI pilots.VMS Event Payload ExamplesExample safety event fields for routing AI detections into VMS alarms, bookmarks, maps, and review queues.Milestone Webhook and Alert ExampleA plain-language example of how an AI safety event can map into a Milestone XProtect alert workflow.Genetec Alarm Routing ExampleA practical example of mapping edge AI safety events into Genetec Security Center alarms and review flows.ONVIF Event Mapping ExampleHow to think about ONVIF event mapping when connecting edge AI detections to existing video workflows.Security One-Pager for Edge AI SafetyA concise security review summary for local inference, raw-video control, event routing, access control, and auditability.Deployment Timeline for Edge AI SafetyA sample 30-day timeline for camera review, edge-node setup, VMS routing, pilot review, and rollout decision.Pilot KPI Worksheet for Safety AIA worksheet for defining pilot event class, camera scope, response path, success metrics, and review cadence.Warehouse Near-Miss Benchmark TemplateA template for collecting warehouse near-miss events by zone, shift, camera, event type, and review outcome.Case Study Template for Edge AI SafetyA case study structure for documenting problem, camera estate, deployment model, pilot metrics, review findings, and approved outcomes.Demo Video Route Structure for Safety AIA route and metadata checklist for publishing demo videos only after real assets, thumbnails, transcripts, and approvals exist.AI Search Prompt Tracking TemplateA template for tracking how AI search systems describe DHI, cite DHI pages, and compare the brand over time.Downloadable Safety AI Pilot DocumentsA source-document checklist for creating downloadable pilot worksheets, scorecards, and review templates.
Glossary
34 routesDefinitions for edge AI safety and CCTV analytics terms.
Edge AI Safety GlossaryDefinitions for edge AI safety, CCTV analytics, VMS integration, ONVIF, RTSP, and workplace safety video analytics.Edge AI safety DefinitionLocal computer vision that detects physical safety events close to the camera stream.CCTV analytics DefinitionSoftware that reads existing camera feeds and turns video into searchable safety events.Near-miss detection DefinitionDetection and indexing of close calls before they become reportable incidents.PPE detection DefinitionComputer vision that checks required safety gear in defined camera zones.RTSP DefinitionA common streaming protocol used by IP cameras and video systems.ONVIF DefinitionA standards-based way for video systems and cameras to interoperate.VMS integration DefinitionRouting safety events into the video management system operators already use.Safety intelligence DefinitionStructured, timely safety context generated from operational signals.Privacy-first video analytics DefinitionVideo analytics designed around local processing and limited data movement.Edge inference DefinitionRunning AI model decisions on local hardware near the camera feed.Camera-to-alert latency DefinitionThe time from an event appearing in video to a usable alert reaching the response path.Forklift-pedestrian detection DefinitionDetection of vehicle and pedestrian paths that may converge in a warehouse or yard.Fall detection from CCTV DefinitionUsing approved camera views to detect collapse or person-down events.Track trespass detection DefinitionDetection of a person or object inside a mapped rail envelope.Platform edge safety DefinitionMonitoring the boundary between safe passenger space and the train path.Visual smoke detection DefinitionUsing existing cameras to detect visible smoke or flame cues in approved zones.Crowd density analytics DefinitionEstimating crowd level, movement, and choke points from camera views.Restricted-zone intrusion DefinitionDetection of people or vehicles entering a mapped controlled area.VMS motion detection DefinitionRule-based pixel-change detection built into many video management systems.Alarm fatigue DefinitionOperator distrust caused by too many low-value or false alarms.Edge node DefinitionLocal compute hardware that runs model inference beside the camera network.Event metadata DefinitionStructured information attached to a detected safety event.Pilot KPI DefinitionA measurable criterion used to decide whether a safety AI pilot worked.On-premise video analytics DefinitionVideo analysis that runs inside the customer environment.Camera estate DefinitionThe full set of cameras, streams, placements, and VMS connections at a site.Operator workflow DefinitionThe path an alert follows from detection to human response.VMS alarm routing DefinitionMapping AI events into VMS alarms, priorities, maps, or review queues.False-positive review DefinitionA structured way to classify and reduce nuisance safety alerts.Existing CCTV readiness DefinitionHow suitable a current camera estate is for a safety AI pilot.Safety AI use-case count DefinitionThe number of incident classes a platform claims to support.AI CCTV safety platform DefinitionA platform that adds safety detection to existing camera systems.Deployment timeline DefinitionThe sequence of steps from camera review to pilot decision.AI crawler DefinitionA bot that fetches public web content for AI search, retrieval, or model-facing indexes.
Integrations
5 routesVMS, camera, and edge hardware fit.
VMS, Camera, and Edge Hardware IntegrationsGenetec, Milestone, Axis, NVIDIA Jetson, RTSP, and ONVIF integration paths for safety AI.Genetec Security Center AI Analytics IntegrationRoute DHI edge safety events into Security Center alarms, maps, bookmarks, and review workflows.Milestone XProtect AI Analytics IntegrationConnect DHI edge detections to XProtect alarms, Smart Client review, and analytics events.Axis Camera AI Analytics IntegrationUse RTSP and ONVIF streams from existing Axis cameras for local safety analytics.NVIDIA Jetson Edge AI Safety AnalyticsRun DHI safety inference close to cameras on local edge hardware.
Blog
8 routesPerspectives from the DHI team.
The DHI BlogPerspectives on edge AI, video privacy, and deploying safety analytics on the cameras you already run.The real divide in camera AI isn't capability. It's control.A US city wants facial recognition on its buses, and the fight that broke out misses the point. The line between safety and surveillance was never the technology. It is who controls it, where it runs, and what it is pointed at.Every warehouse has cameras. Almost none have early warning.A Los Angeles cold-storage warehouse burned for eight days. The hard lesson for safety teams isn't about fire codes: it's the gap between a camera that records an incident and one that catches it early enough to matter.Who can turn your AI off?In June 2026, the most powerful AI models on the market were gated, pulled, and partially reinstated by parties their customers don't control. For most software that's a policy story. For safety-critical systems, it's an ownership question.Nobody answers the alarm anymoreWhen most camera alerts are shadows, rain, and headlights, your team learns to ignore all of them, including the one that mattered. Why alarm fatigue is a detection problem, not a discipline problem.You don't need new camerasRip-and-replace is what kills safety projects. How to add AI to the CCTV and VMS you already run, starting with a single camera.Your "edge AI" might just be the cloud with extra stepsA cloud provider just discontinued its edge-vision product. Here's what it reveals about how most camera AI is really built, and the one question that separates real edge from rented edge.Where does your video actually go?The question that quietly stalls AI camera projects, and why keeping footage on-site turns privacy from a liability into the default.