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Get Started

Loitering Detection leverages advanced AI algorithms to monitor and analyze real-time video feeds, identifying individuals lingering in designated areas. It provides a modular architecture that integrates seamlessly with various input sources and leverages AI models to deliver accurate and actionable insights.

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 pipeline to see loitering detection in action.
  • Access the application's features and user interfaces: Explore the Grafana dashboard, Node-RED interface, and DL Streamer Pipeline Server to monitor, analyze and customize workflows.

Prerequisites

Set up and first use

  1. Clone the Suite:

    Go to the target directory of your choice and clone the suite. If you want to clone a specific release branch, replace main with the desired tag. To learn more on partial cloning, check the Repository Cloning guide.

    git clone --filter=blob:none --sparse --branch main https://github.com/open-edge-platform/edge-ai-suites.git
cd edge-ai-suites
git sparse-checkout set metro-ai-suite
cd metro-ai-suite/metro-vision-ai-app-recipe/

  2. Setup Application and Download Assets:

    • Use the installation script to configure the application and download required models:

      ./install.sh loitering-detection

    Note: For environments requiring a specific host IP address (such as when using Edge Manageability Toolkit or deploying across different network interfaces), you can explicitly specify the IP address: ./install.sh loitering-detection <HOST_IP> (Replace <HOST_IP> with your target IP address.)

Run the application

  1. Start the Application:

    • Download container images with Application microservices and run with Docker Compose:

      docker compose up -d
      Check Status of Microservices - 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)

  2. Run Predefined Pipelines:

    • Start video streams to run video inference pipelines:

      ./sample_start.sh

    • To check the status of the pipelines:

      ./sample_status.sh
      Stop pipelines - To stop the pipelines without waiting for video streams to finish replay: > **NOTE:** This will stop all the pipelines and the streams. **DO NOT** run this if you want to see loitering detection ```bash ./sample_stop.sh ```

  3. View the Application Output:

    • Open a browser and go to https://localhost/grafana to access the Grafana dashboard.
      • Change the localhost to your host IP if you are accessing it remotely.
    • Log in with the following credentials:
      • 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.

Access the Application and Components

Nginx Dashboard

  • URL: https://localhost

Grafana UI

  • URL: https://localhost/grafana

  • Log in with credentials:

    • Username: admin
    • Password: admin (You will be prompted to change it on first login.)

  • In Grafana UI, the dashboard displays detected people and cars Grafana Dashboard

    Note: In the default pipeline, we use gvatrack tracking-type=short-term-imageless element. Imageless tracking forms object associations based on the movement and shape of objects, and it does not use image data. Since it does not use image features, the same object may receive different IDs over time due to lack of re-identification.

NodeRED UI

  • URL: https://localhost/nodered/

DL Streamer Pipeline Server

  • REST API: https://localhost/api/pipelines/status
  • WebRTC: https://localhost/mediamtx/object_tracking_1/

Stop the Application

  • To stop the application microservices, use the following command:

    docker compose down

Other Deployment Options

Choose one of the following methods to deploy the Loitering Detection Sample Application:

Next Steps

Troubleshooting

  1. Changing the Host IP Address

    • If you need to use a specific Host IP address instead of the one automatically detected during installation, you can explicitly provide it using the following command. Replace <HOST_IP> with your desired IP address:

      ./install.sh <HOST_IP>

  2. Containers Not Starting:

    • Check the Docker logs for errors:

      docker compose logs

  3. No Video Streaming on Grafana Dashboard

    • Go to the Grafana "Video Analytics Dashboard".
    • Click on the Edit option (located on the right side) under the WebRTC Stream panel.
    • Update the URL from http://localhost:8083 to http://host-ip:8083.

  4. Failed Grafana Deployment

    • If unable to deploy the Grafana container successfully due to fail to GET "https://grafana.com/api/plugins/yesoreyeram-infinity-datasource/versions": context deadline exceeded, please ensure the proxy is configured in the ~/.docker/config.json as shown below:

              "proxies": {
                "default": {
                        "httpProxy": "<Enter http proxy>",
                        "httpsProxy": "<Enter https proxy>",
                        "noProxy": "<Enter no proxy>"
                }
        }

    • After editing the file, remember to reload and restart docker before deploying the microservice again.

      systemctl daemon-reload
systemctl restart docker

Supporting Resources