AI that Helps Operators See What Matters
Cities, critical sites, and public venues run on missions that cannot fail. Intel delivers real-time intelligence at the crime center, the perimeter, and the command room, with the security, integration, and governance these environments demand.
Automation for the Missions that Cannot Fail
Safety and security teams are stretched thin: more cameras, more public scrutiny, the same number of operators on the bench. Intel provides one compute foundation for video analytics, access control, and governance, backed by the VMS and SOC ecosystem these teams already use — no forklift upgrade required. Proven on-premises and across the long support cycles public agencies plan around, Intel helps operators act, not just watch, without compromising public trust.
Cover more ground,
same headcount
Operators cannot watch every feed. AI running on the camera and gateway surfaces the events that matter, so a smaller team covers a larger footprint without staring at static.
From alarm to action in seconds
Inference at the asset moves alerts from alarm to dispatch in seconds, not minutes. Automate triage of intrusions, gunshots, and threat alerts to the right responders before incidents escalate.
Decisions your oversight can defend
Every high-stakes alert is reviewed by a trained operator before any action. Audit logs are complete, inference stays on-premises, and accountability stays where it belongs.
Built for Public Safety, Sites, and Public Spaces
Each safety and security mission has its own pace, jurisdiction, and risk threshold. Yet all three depend on real-time intelligence at the edge, governance the public can trust, and integration with the VMS and SOC tools their teams already run.
Public Safety and Policing
Public-safety agencies are facing smaller departments, larger jurisdictions, and rising pressure to cut response time without overstepping civil liberties. Agentic AI at the real-time crime center fuses video, license-plate reads, gunshot alerts, and computer-aided dispatch into one operating picture, with forensic search across hours of footage in seconds and human-in-the-loop validation on any alert that could trigger an armed response.
Use Cases
- Real-time crime center fusion of video, LPR, and CAD
- Forensic video search across hours of footage in seconds
- Human-in-the-loop validation for weapon and threat alerts
- Comprehensive audit logs for evidence tracking and oversight
- Predictive analytics with bias and fairness review built in
Critical Site Protection
Substations and data centers, ports and water plants, government buildings and corporate campuses all share the same perimeter problem: too much fence line, too many gates, too few people watching. Run inferencing workloads at the perimeter to integrate cameras, sensors, access control, and intrusion detection on one on-premises dashboard, so footage and credentials never leave the site, with automated lock-down workflows and a hardened cybersecurity posture against emerging threats.
Use Cases
- Integrated perimeter cameras, sensors, and access control
- Real-time intrusion detection with automated lock-down
- Multi-factor access control with badge and biometric options
- Network segmentation and VMS cybersecurity hardening
- 24/7 monitoring for unmanned and remote critical sites
Crowd, Venues & Events
From stadium operations to one-time events like the World Cup, the playbook is similar: weeks or years of multi-agency planning, a unified command center on game day, and forensic playback the next morning. Use agentic AI for multimodal workloads at the far edge to combine camera feeds, license-plate streams, drone data, and sensor inputs, powering crowd-density estimation, behavior analytics, drone detection, and forensic search across the VMS and SOC tools host cities, venues, and federal partners already use.
Use Cases
- Apron and gate sensor fusion for airport operations
- Container yard and crane scheduling at the seaport
- Biometric screening and baggage tracking automation
- Predictive rail and autonomous-vehicle coordination
- Digital twin simulation for capital planning and ops
See What's Possible When Cameras Understand
Real deployments where Intel-powered edge AI is helping public-safety teams, site operators, and venue managers see more, decide better, and act with the right oversight.
Real-Time Crime Centers, Proven in the Field
Albuquerque's real-time crime center, built with Genetec Citigraf on Intel, fuses city video, license-plate reads, gunshot detection, CAD dispatch, and GIS mapping into one operating picture — cutting time from event to dispatch while keeping audit logs civilian-oversight bodies can actually inspect.
City-Scale Intelligent CCTV in Taipei
MiTAC and Intel delivered the Taipei City Police Department's intelligent CCTV system — the largest urban safety deployment in Taiwan — consolidating AI, ML, and video analytics so officers process more footage faster and more accurately across the city.
Spatial Awareness from the Cameras You Own
Axis cameras with Intel SceneScape turn flat video into spatial awareness — tracking people and objects across a site in real time for threat detection and perimeter protection, on the camera estates operators already run.
Open Video Analytics, Validated on Intel
Irisity's IRIS+ runs validated video analytics for security, safety, and business insight on Intel — open, scalable, and deployable on the hardware operators already own, no discrete-GPU farm required.
Crowd Intelligence for Venues and Events
WaitTime's crowd analytics, validated on Intel, give venue and event operators real-time density and flow intelligence — turning camera feeds into safer crowds and smoother operations at stadium and event scale.
Insights from the Edge
Intel ECG voices on what it takes to run AI at the edge across a city's systems — agentic and hybrid, sovereign and real-time — from traffic to public safety to utilities.
Your cameras have watched for years; this is what it takes for them to understand. How edge computer vision moves past pattern matching to reading scenes — intent, behavior, and context — across a security estate in real time.
Why public infrastructure AI must stay local and secure end to end, keeping video, biometrics, and audit trails under agency governance across the boot-to-inference lifecycle, while still enabling hybrid-cloud reporting.
Privacy-first surveillance starts with where the footage stays. Why keeping inference and evidence local and in-jurisdiction is an architectural choice — and how it keeps agencies defensible to the public and the oversight bodies that review them.
More AI, less hardware. Why the next generation of video security runs on integrated AI SoCs — sensors to servers, silicon included — rather than racks of discrete GPUs, and what that does to cost, power, and footprint.
Right-Sized Compute, Open Systems, and Software Built for the Edge
Deploy edge applications quickly with Intel's portfolio of edge-ready compute and connectivity technologies. Enhanced processing at the edge to get critical insights and business value from your data with compute resources where you need them most
Metro AI Suite is a powerful software framework that empowers Intel's hardware and software ecosystem to rapidly build, configure, optimize and evaluate Visual AI and Gen AI platforms and solutions. With sample applications like Smart Search, Sensor Fusion, and Video Summarization, Metro fast-tracks development and reduces TCO, driving intelligent, scalable, and performant edge solutions
Built Open. Proven at Scale. Ready When You Are.
You don’t have to start from scratch. With Intel’s ecosystem, product-ready solutions and experience backed by 100,000+ real-world deployments, we can help you define a smart infrastructure project without the engineering risk or vendor lock-in.
FAQs
Frequently Asked Questions
Integrated AI acceleration means the AI processing hardware, GPU and NPU, is built directly into the processor rather than requiring a separate, add-on graphics card — so video security runs on integrated AI SoCs, not racks of discrete GPUs. Intel Core Ultra combines CPU, GPU, and NPU on a single chip, while Intel Xeon 6 brings high-throughput CPU plus integrated GPU acceleration to edge servers, with discrete accelerators added only where larger workloads require them. Intel OpenVINO targets the right engine for each layer of a model, reducing system cost, power draw, and the need for device-specific rewrites.
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Edge AI processes video and biometric data on-premises, inside the agency's jurisdiction — supporting GDPR data-minimization and the EU AI Act. The Act's tiers matter: it prohibits most real-time remote biometric identification in public spaces (with narrow law-enforcement exceptions) and treats other biometric and law-enforcement AI as high-risk, so running locally does not make a prohibited use lawful. For permitted uses, on-prem inference, audit logging, and human-in-the-loop review support compliance — but the legal posture is the operator's, set with counsel.
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Yes, with right-sized, quantized models. Generative AI and LLMs run at the edge on Intel Core Ultra (CPU, GPU, and NPU on one chip) for natural-language video search, alert summarization, and report drafting; Intel Xeon 6 with integrated acceleration — or a discrete GPU — handles larger models at the operations center. Intel OpenVINO compresses and targets models to an edge power and memory budget. The realistic pattern is local inference for privacy-sensitive, latency-critical work, with the cloud reserved for the very largest models.
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Multi-modal AI fuses more than one data type — video, audio, radar, LiDAR, access-control events, and environmental sensors — into one understanding of a scene. It matters because no single sensor is enough: audio catches a gunshot a camera misses, radar and LiDAR track in darkness or weather, and access logs explain who opened a door. Fusing them at the edge cuts false alarms and gives operators one corroborated picture. Intel's integrated CPU, GPU, and NPU and the Metro AI Suite's sensor-fusion apps are built for these pipelines.
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TOPS (tera-operations per second) measure a chip's peak throughput — but they don't predict real video-analytics performance on their own. What decides it is sustained throughput on your actual models and resolutions, memory bandwidth, how well the software targets CPU, GPU, and NPU, and end-to-end latency including video decode and pre/post-processing. A part with fewer headline TOPS but better software and memory can beat a higher-TOPS one on live streams. Benchmark on representative channels, models, and frame rates — not the spec sheet.
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Intel publishes validated, benchmarked reference configurations rather than peak specs. Partner validation reports on builders.intel.com — for analytics and VMS platforms such as ISS, AxxonSoft, and Irisity — document stream-handling and throughput on named Intel servers, and the Intel performance index lists benchmarks with full configurations. Because results vary by model, resolution, and channel count, benchmark on your own representative workload; Intel and its partners provide the validated baselines to start from.
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You don't rip and replace. Most migrations are incremental: keep existing cameras and VMS, add an Intel-based analytics layer alongside them, and convert one site or use case at a time. Intel-based platforms integrate with the VMS, NVR, and SOC tools already in place, so footage and workflows continue while AI is layered on. Standardize on the Open Edge Platform for remote management, prove value on a pilot, then expand on a refresh schedule the budget allows — no forklift upgrade.
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