add references

Author: kblaszczak-intel

metro-ai-suite/metro-vision-ai-app-recipe/smart-intersection/docs/user-guide/get-started.md

@@ -1,38 +1,30 @@
 # Get Started
 
-<!--
-**Sample Description**: Provide a brief overview of the application and its purpose.
--->
-The Smart Intersection Sample Application is a modular sample application designed to help developers create intelligent intersection monitoring solutions. By leveraging AI and sensor fusion, this sample application demonstrates how to achieve accurate traffic detection, congestion management, and real-time alerting.
-
-<!--
-**What You Can Do**: Highlight the developer workflows supported by the guide.
--->
-By following this guide, you will learn how to:
-- **Set up the sample application**: Use Docker Compose to quickly deploy the application in your environment.
-- **Run a predefined pipeline**: Execute a sample pipeline to see real-time transportation monitoring and object detection in action.
-- **Access the application's features and user interfaces**: Explore the Intel® SceneScape Web UI, Grafana dashboard, Node-RED interface, and DL Streamer Pipeline Server to monitor, analyze and customize workflows.
-
-## Prerequisites
+The Smart Intersection Sample Application is a modular sample application designed to help
+developers create intelligent intersection monitoring solutions. By leveraging AI and sensor
+fusion, this sample application demonstrates how to achieve accurate traffic detection,
+congestion management, and real-time alerting.
+
+To get started:
+- **Set up the sample application**: use Docker Compose to quickly deploy the application in
+  your environment.
+- **Run a predefined pipeline**: execute a sample pipeline to see real-time transportation
+  monitoring and object detection in action.
+- **Access the application's features and user interfaces**: explore the Intel® SceneScape
+  Web UI, Grafana dashboard, Node-RED interface, and DL Streamer Pipeline Server to monitor,
+  analyze and customize workflows.
+- **Consider Enabling Security features**: use hardware-based security measures to make your
+  application safer.
+
+
+## Setup and First Use
+
+**Prerequisites**
 - Verify that your system meets the [minimum requirements](./get-started/system-requirements.md).
 - Install Docker: [Installation Guide](https://docs.docker.com/get-docker/).
-- Enable running docker without "sudo": [Post Install](https://docs.docker.com/engine/install/linux-postinstall/)
-- Install Git: [Installing Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+- Enable running docker without "sudo": [Post Install](https://docs.docker.com/engine/install/linux-postinstall/).
+- Install Git: [Installing Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git).
 
-<!--
-**Setup and First Use**: Include installation instructions, basic operation, and initial validation.
--->
-## Set up and First Use
-
-<!--
-**User Story 1**: Setting Up the Application
-- **As a developer**, I want to set up the application in my environment, so that I can start exploring its functionality.
-
-**Acceptance Criteria**:
-1. Step-by-step instructions for downloading and installing the application.
-2. Verification steps to ensure successful setup.
-3. Troubleshooting tips for common installation issues.
--->
 
 1. **Clone the Repository**:
    - Run:
@@ -65,25 +57,25 @@ By following this guide, you will learn how to:
      docker compose up -d
      ```
 
-     <details>
-     <summary>
-     Check Status of Microservices
-     </summary>
+   <details>
+   <summary>
+   Check Status of Microservices
+   </summary>
 
-     - The application starts the following microservices.
-     - To check if all microservices are in Running state:
-       ```bash
-       docker ps
-       ```
+   - The application starts the following microservices.
+   - To check if all microservices are in Running state:
+     ```bash
+     docker ps
+     ```
 
-     **Expected Services:**
-     - Grafana Dashboard
-     - DL Streamer Pipeline Server
-     - MQTT Broker
-     - Node-RED (for applications without Intel® SceneScape)
-     - Intel® SceneScape services (for Smart Intersection only)
+   **Expected Services:**
+   - Grafana Dashboard
+   - DL Streamer Pipeline Server
+   - MQTT Broker
+   - Node-RED (for applications without Intel® SceneScape)
+   - Intel® SceneScape services (for Smart Intersection only)
 
-     </details>
+   </details>
 
 2. **View the Application Output**:
    - Open a browser and go to `https://localhost/grafana/` to access the Grafana dashboard.
@@ -92,11 +84,12 @@ By following this guide, you will learn how to:
      - **Username**: `admin`
      - **Password**: `admin`
    - Check under the Dashboards section for the application-specific preloaded dashboard.
-   - **Expected Results**: The dashboard displays real-time video streams with AI overlays and detection metrics.
+   - **Expected Results**: The dashboard displays real-time video streams with AI overlays
+     and detection metrics.
 
-## **Access the Application and Components** ##
+## Access the Application and Components
 
-### **Application UI** ###
+### Application UI
 
 Open a browser and go to the following endpoints to access the application. Use `<actual_ip>`
 instead of `localhost` for external access:
@@ -114,34 +107,48 @@ instead of `localhost` for external access:
 > **Note**:
 > - After starting the application, wait approximately 1 minute for the MQTT broker to initialize. You can confirm it is ready when green arrows appear for MQTT in the application interface. Since the application uses HTTPS, your browser may display a self-signed certificate warning. For the best experience, use **Google Chrome**.
 
-### **Grafana UI** ###
+### Grafana UI
+
 - **URL**: [https://localhost/grafana/](https://localhost/grafana/)
 - **Log in with credentials**:
     - **Username**: `admin`
     - **Password**: `admin` (You will be prompted to change it on first login.)
 
-### **InfluxDB UI** ###
+### InfluxDB UI
+
 - **URL**: [http://localhost:8086](http://localhost:8086)
 - **Log in with credentials**:
     - **Username**: `<your_influx_username>` (Check `./smart-intersection/src/secrets/influxdb2/influxdb2-admin-username`)
     - **Password**: `<your_influx_password>` (Check `./smart-intersection/src/secrets/influxdb2/influxdb2-admin-password`).
 
-### **NodeRED UI** ###
+### NodeRED UI
+
 - **URL**: [https://localhost/nodered/](https://localhost/nodered/)
 
-### **DL Streamer Pipeline Server** ###
+### DL Streamer Pipeline Server
+
 - **REST API**: [https://localhost/api/pipelines/status](https://localhost/api/pipelines/status)
   - **Check Pipeline Status**:
     ```bash
     curl -k https://localhost/api/pipelines/status
     ```
 
+
 ## Verify the Application
 
-- **Fused object tracks**: In Scene Management UI, click on the Intersection-Demo card to navigate to the Scene. On the Scene page, you will see fused tracks moving on the map. You will also see greyed out frames from each camera. Toggle the "Live View" button to see the incoming camera frames. The object detections in the camera feeds will correlate to the tracks on the map.
-      ![Intersection Scene Homepage](./_assets/scenescape.png)
-- **Grafana Dashboard**: In Grafana UI, observe aggregated analytics of different regions of interests in the grafana dashboard. After navigating to Grafana home page, click on "Dashboards" and click on item "Anthem-ITS-Data".
-      ![Intersection Grafana Dashboard](./_assets/grafana.png)
+- **Fused object tracks**: in Scene Management UI, click on the Intersection-Demo card to
+  navigate to the Scene. On the Scene page, you will see fused tracks moving on the map. You
+  will also see greyed out frames from each camera. Toggle the "Live View" button to see the
+  incoming camera frames. The object detections in the camera feeds will correlate to the
+  tracks on the map.
+
+  ![Intersection Scene Homepage](./_assets/scenescape.png)
+
+- **Grafana Dashboard**: In Grafana UI, observe aggregated analytics of different regions of
+  interests in the grafana dashboard. After navigating to Grafana home page, click on
+  "Dashboards" and click on item "Anthem-ITS-Data".
+
+  ![Intersection Grafana Dashboard](./_assets/grafana.png)
 
 ## **Stop the Application**:
   - To stop the application microservices, use the following command:
@@ -153,10 +160,20 @@ instead of `localhost` for external access:
 
 Choose one of the following methods to deploy the Smart Intersection Sample Application:
 
-- **[Deploy Using Helm](./get-started/deploy-with-helm.md)**: Use Helm to deploy the application to a Kubernetes cluster for scalable and production-ready deployments.
+- **[Deploy Using Helm](./get-started/deploy-with-helm.md)**: Use Helm to deploy the
+  application to a Kubernetes cluster for scalable and production-ready deployments.
+
+## Security Enablement
+
+With AI systems handling sensitive city data and making autonomous decisions, robust security
+is essential. Intel platforms provide built-in security features to protect data, infrastructure,
+and AI processing. See the [Security Enablement Guide](https://docs.openedgeplatform.intel.com/2026.0/OEP-articles/application-security.html)
+that uses the example of Smart Intersection to show how to secure Open Edge Platform
+applications.
 
-## Resources
+## Learn More
 
+- [Security Enablement Guide](https://docs.openedgeplatform.intel.com/2026.0/OEP-articles/application-security.html)
 - [Troubleshooting](./troubleshooting.md): Find detailed steps to resolve common issues during deployments.
 - [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/index.html): Intel microservice based on Python for video ingestion and deep learning inferencing functions.
 - [Intel® SceneScape](https://docs.openedgeplatform.intel.com/dev/scenescape/index.html): Intel Scene-based AI software framework.

metro-ai-suite/metro-vision-ai-app-recipe/smart-intersection/docs/user-guide/index.md

@@ -14,26 +14,37 @@
 </div>
 hide_directive-->
 
-Smart Intersection Sample Application uses edge AI to improve traffic flow. It combines feeds from multiple cameras to track vehicles across angles, analyze speed and direction, and understand interactions in real space. The system can run on existing cameras and deliver real-time, coordinated insights for smarter traffic monitoring.
+The Smart Intersection sample application uses edge AI to improve the traffic flow.
+It combines feeds from multiple cameras to track vehicles from different angles, analyze their
+speed and direction, and understand interactions in real space. The system can be implemented
+with existing cameras and deliver real-time, coordinated insights for smarter traffic monitoring.
 
 **Example Use Cases**
 
-- **Pedestrian Safety**: Enhance safety for people crossing the street. The system tracks pedestrians at crosswalks and generates alerts when people walk outside safe crossing areas.
-- **Traffic Flow Monitoring**: Count vehicles and measure dwell time in each lane, detecting when vehicles stay too long in lanes. This identifies stalled cars, accidents, and traffic jams.
+- **Pedestrian Safety**: enhance safety for people crossing the street. The system tracks
+  pedestrians at crosswalks and generates alerts when people walk outside safe crossing areas.
+- **Traffic Flow Monitoring**: count vehicles and measure dwell time in each lane, detecting
+  when vehicles stay in their lanes for too long. This identifies stalled cars, accidents,
+  and traffic jams.
 
 **Key Benefits**
 
-The key benefits are as follows:
+- **Multi-camera multi-object tracking**: enables tracking of objects across multiple camera
+  views.
+- **Scene-based analytics**: regions of interest that span multiple views can be easily
+  defined on the map rather than independently on each camera view. This greatly simplifies
+  business logic, enables more flexibility in defining regions, and enables additional sensors
+  such as lidar and radar to be used to track vehicles and people.
+- **Improved Urban Management**: object tracking and analytics are available near-real-time on
+  MQTT broker to enable actionable insights for traffic monitoring and safety applications.
+- **Reduced TCO**: works with the existing cameras, and makes scaling with additional sensors
+  and cameras easy. This simplifies business logic development, and future-proofs the solution.
 
-- **Multi-camera multi-object tracking**: Enables tracking of objects across multiple camera views.
-- **Scene based analytics**: Regions of interest that span multiple views can be easily defined on the map rather than independently on each camera view. This greatly simplifies business logic, enables more flexibility in defining regions, and allows, in addition to cameras, various types of sensors such as lidar and radar to be used to track vehicles and people.
-- **Improved Urban Management**: Object tracking and analytics are available near-real-time on the MQTT broker to enable actionable insights for traffic monitoring and safety applications.
-- **Reduced TCO**: Works with existing cameras, simplifies business logic development, and future-proofs the solution by enabling additional sensors and cameras as needed without changing the business logic.
 
-This guide is designed to help developers understand the architecture, setup, and customization of the sample application.
 
 ## Learn More
 
+- [Security Enablement](https://docs.openedgeplatform.intel.com/2026.0/OEP-articles/application-security.html)
 - [Intel® SceneScape](https://docs.openedgeplatform.intel.com/dev/scenescape/index.html): Intel Scene-based AI software framework.
 - [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/index.html): Intel microservice based on Python for video ingestion and deep learning inferencing functions.