Too Much Raw Video, Too Little Prioritization
Operations teams need relevant events and context—not another wall of video streams requiring continuous manual review.
Advantech × StarTech · Saudi Smart City & Urban Intelligence Webinar
Next-Gen Saudi Urban Intelligence
Deploying VisionSense and Edge AI for Safer Roads, Smarter Mobility, and Public Safety
Explore how a connected smart-city architecture can transform selected CCTV and roadside camera feeds into actionable intelligence using Edge AI, industrial networking, and VisionSense—then move from technical evaluation to a locally supported PoC.
Free registration · Live VisionSense demonstration · English session · Local Saudi PoC follow-up
DateMonday, September 14, 2026
Time11:00 AM GST / 10:00 AM KSA
LanguageEnglish · 60 Minutes
HostTamer Aldawi · KSA Channel Manager, Advantech
Why Attend
See the Architecture. Watch the Demo. Define Your PoC.
A focused technical session for smart-city leaders, transportation teams, system integrators, and engineering consultants evaluating how to add Edge AI to existing roadside and CCTV infrastructure.
01
See how Intel/x86 and NVIDIA Jetson platforms can take complementary roles alongside industrial networking, cellular connectivity, field-data integration, and control-center or cloud systems.
02
Watch VisionSense process selected video and surface traffic or public-safety events for operational review.
03
Learn what information is required to scope a locally supported PoC with Advantech and StarTech.
Register for the Webinar
Register to receive the webinar access details and the option to request a follow-up discussion for your use case. Please noted that yahoo and gmail are not accepted.
Saudi Smart City Momentum
Turn Distributed Video into Timely Operational Insight
Saudi smart-city and transportation programs increasingly depend on distributed cameras, roadside systems, and control centers. The challenge is not capturing more video—it is processing selected feeds locally, surfacing relevant events, and connecting those events to existing operational workflows. This session is built for government program leaders, transportation and command-center teams, system integrators, solution providers, and engineering consultants.
Customer Challenges
Three Barriers Between Video and Operational Action
The objective is not to centralize every stream. It is to identify the right events, at the right locations, and deliver useful information to the teams responsible for response.
Distributed Sites and Centralized Processing Pressure
Sending every feed to a central platform can increase bandwidth, storage, latency, and operational complexity. Edge inference keeps selected processing closer to the source.
Modernization Without Disrupting Existing Systems
New analytics must work with existing cameras, networks, and control-center platforms. Edge AI can be introduced at selected infrastructure points instead of requiring a full replacement.
Technical Takeaways
Leave with a Clear Evaluation Framework
The session is structured to help you evaluate technical fit, integration requirements, and the next step for a transportation or public-safety Edge AI use case.
01
How x86 and Jetson Can Take Complementary Roles
Understand how Intel/x86 platforms can support application workloads and system integration, while NVIDIA Jetson platforms can accelerate real-time AI inference, depending on the solution design.
02
How Roadside Systems Connect Edge to Cloud
Follow the path across cameras, MIC edge platforms, EKI industrial Ethernet, ICR cellular connectivity, WISE-EdgeLink field-data integration, and cloud or control-center systems.
03
How VisionSense Turns Video into Events
See how an NVIDIA Jetson Edge AI platform and VisionSense can process selected video and convert configured people, object, or event analytics into information for operational review.
04
What Is Needed to Scope a PoC
Understand the use-case inputs, component decisions, validation criteria, and integration questions required before deployment planning.
Edge-to-Cloud Solution Architecture
Build the Roadside Edge-to-Cloud Foundation
Review an illustrative heterogeneous architecture in which Intel/x86 edge computing and NVIDIA Jetson AI inference can be combined with industrial Ethernet, cellular connectivity, field-data integration, remote management, and cloud or control-center systems.
Connect Cameras, Sensors, and Roadside Systems
Bring selected CCTV, IP cameras, roadside cameras, smart poles, sensors, and field-system data into the architecture.
CCTV · IP cameras · Roadside cameras · Smart poles · Field sensors
Assign x86 and Jetson Edge Workloads
Depending on the project design, use Intel/x86 computing for application workloads and system integration, while NVIDIA Jetson accelerates real-time AI vision inference near the video source.
MIC-770 V3 · MIC-733-TS · Intel/x86 · NVIDIA Jetson
Connect, Collect, and Manage Distributed Assets
Use industrial Ethernet, cellular connectivity, protocol conversion, field-data integration, and platform-appropriate remote management across distributed sites.
EKI industrial Ethernet · ICR 4G/5G · WISE-EdgeLink · DeviceOn or Allxon, depending on platform
Integrate with Cloud and Operational Workflows
Deliver selected data and AI events to cloud services, traffic platforms, and command centers, with possible downstream integration to PIS, digital signage, or remote update workflows where required by the project.
VisionSense · Cloud · Command center · PIS · Digital signage
Real Use Cases · Live Demo
Watch the Complete Edge-to-Event Workflow
During the VisionSense live demo, see how a selected roadside or public-space video feed is processed locally, how configured people, objects, or events are identified, and how the resulting information can be presented for operational review. The exact analytics models and demo configuration will be confirmed by the presenting team.
Proposed Demonstration ComponentsMIC-733-TS · ICAM-540 / ICAM-520 · VisionSense · Final configuration to be confirmed
60-Minute Agenda
What You Will Learn in 60 Minutes
A focused progression from Saudi market context to a heterogeneous x86 and NVIDIA Jetson Edge-to-Cloud architecture, rugged roadside AI deployment, a live VisionSense demonstration, and a practical local PoC path.
Webinar schedule
Monday, September 14, 2026
11:00 AM GST / 10:00 AM KSA
SESSION 015 min
Welcome and Webinar Overview
Host: Tamer Aldawi · KSA Channel Manager, Advantech
Session focusSet the context for the webinar, introduce the speakers, and explain how the session connects Saudi transportation priorities, Edge AI, VisionSense, and local deployment support.
What you’ll take awayA clear view of the session flow and the practical questions each segment will answer.
SESSION 0210 min
Saudi Smart City and Transportation Momentum
Presenter: Tamer Aldawi · KSA Channel Manager, Advantech
Session focusReview the infrastructure demands behind expanding smart-city, transportation, and public-safety programs across distributed urban locations.
What you’ll take awayWhy timely local processing, scalable integration, and local deployment support matter to evolving city-operation requirements.
SESSION 0310 min
From Roadside Edge to Cloud: Building the Foundation for Intelligent Transportation
Presenter: Andy Hsu · Product Manager, Advantech
Session focusIntroduce an illustrative Intel/x86 and NVIDIA Jetson architecture and show how edge computing, industrial Ethernet, cellular connectivity, protocol conversion, field-data integration, and remote management can connect roadside systems to cloud and control-center applications. PIS, digital signage, and remote update workflows are presented as possible downstream integrations where relevant.
What you’ll take awayA clear understanding of the potential roles played by MIC-770 V3, NVIDIA Jetson platforms, EKI, ICR, and WISE-EdgeLink within an Edge-to-Cloud solution design.
SESSION 0410 min
NEMA TS2 Jetson Edge AI for Roadside Vision and Public Safety
Presenter: Grace Lai · Senior Product Manager, Advantech
Session focusTake a closer look at MIC-733-TS, a NEMA TS2-compliant NVIDIA Jetson AGX Orin AI inference system designed for wide-temperature roadside and transportation-infrastructure deployments, including optional PoE connectivity for multiple IP-camera inputs.
What you’ll take awayHow a transportation-oriented Jetson platform can support real-time AI vision workloads and connect to a AI Vision based analytics workflow, subject to the final camera and software configuration.
SESSION 0510 min
VisionSense Live Demo: From Video Analytics to Operational Events
Presenter: Jordan Wu · Product Manager, Advantech
Session focusFollow a selected video feed through local inference, configured people, object, or event identification, and presentation of the resulting information for operational review.
What you’ll take awayA concrete view of how VisionSense can turn RTSP video from existing CCTV systems into configured analytics and alerts for transportation or public-space operational workflows.
SESSION 0610 min
From Use Case to Deployment: The Local PoC Program
Presenter: Samveed Varma · Sales Manager of KSA, StarTech
Session focusShow how to define the camera source, target event, operating environment, integration point, validation criteria, and support roles for a local technical evaluation.
What you’ll take awayThe information your organization should prepare to begin a structured PoC discussion with Advantech and StarTech.
SESSION 075 min
Q&A and Next Steps
Presenter: All speakers
Session focusAsk technical, integration, hardware-selection, deployment, and local-support questions based on your organization’s requirements.
What you’ll take awayA clearer next step: technical discovery, a PoC discussion, or a focused solution follow-up.
From Use Case to Deployment
Bring a Use Case. Leave with a PoC Starting Point.
Organizations with a defined transportation or public-safety requirement can request a follow-up technical discovery discussion with Advantech and StarTech to review the camera source, target event, integration point, and PoC success criteria.
Define the Operational Objective
Clarify the camera source, target event, operating environment, responsible team, and expected operational outcome.
Select the Evaluation Architecture
Match the use case with suitable Edge AI computing, camera input, industrial networking, and VisionSense components.
Agree on Success Criteria
Define what will be evaluated, including event quality, technical fit, integration requirements, and deployment constraints.
Prepare the Next Deployment Decision
Document responsibilities, rollout assumptions, support requirements, and the technical or commercial next step.
