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.

Prerequisites#

Verify that your system meets the minimum requirements.
Install Docker: Installation Guide.

Set up and First Use#

Download the Application:

Download the Docker Compose file and configuration:

git clone https://github.com/open-edge-platform/edge-ai-suites.git
cd edge-ai-suites/metro-ai-suite/loitering-detection/

Configure the Application and Download Assets
- Download the Models and Video files.
- Configure the provided application IP address. If omitted, the application uses the primary IP address.
```
./install.sh [<IP_address>]
```
  Check installed assets.
  
  The install.sh script downloads the following assets:
  
  Models
  - pedestrian-and-vehicle-detector-adas-0001: This model is obtained from the Open Model Zoo.
  Videos
  
  Video Name
  
  Download URL
  
  VIRAT_S_000101.mp4
  
  VIRAT_S_000101.mp4
  
  VIRAT_S_000102.mp4
  
  VIRAT_S_000102.mp4
  
  VIRAT_S_000103.mp4
  
  VIRAT_S_000103.mp4
  
  VIRAT_S_000104.mp4
  
  VIRAT_S_000104.mp4

Run the Application#

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, see also How it Works.
  - To check if all microservices are in Running state:
    docker ps
Run Predefined Loitering Detection Pipelines:
- Start video streams to run Loitering Detection pipelines:
```
./sample_start.sh
```
  Check Status and Stop pipelines
  - To check the status:
    ./sample_status.sh
  - To stop the pipelines without waiting for video streams to finish replay:
    ./sample_stop.sh
View the Application Output:
- Open a browser and go to http://localhost:3000 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 default dashboard named “Video Analytics Dashboard”.
- Expected Results: The dashboard displays detected people and cars.
Stop the Application:
- To stop the application microservices, use the following command:
```
docker compose down -v
```

Next Steps#

How to Customize the Application

Troubleshooting#

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>
```
Containers Not Starting:
- Check the Docker logs for errors:
```
docker compose logs
```
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.

Failed Grafana Deployment

If unable to deploy 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
```