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.
Install Python pip and venv packages

  sudo apt update && sudo apt install -y python3-pip python3-venv

Set up and First Use#

Download the Compose File:
- Download the Docker Compose file and configuration:
```
  git clone https://github.com/intel/metro-ai-suite.git
```
Navigate to the Directory:
- Go to the directory where you saved the Compose file:
```
cd metro-ai-suite/loitering-detection/
```
Update the IP Address:
- Open the .env file:
```
nano .env
```
- Modify the HOST_IP variable in the .env file to your actual host IP address.
```
HOST_IP=10.10.10.10
```
- Save and close the file.
Update Dashboard with your Host IP Address and Use Case
```
    ./update_dashboard.sh
```
Download the Model and Video files
```
    ./install.sh
```
Start the Application:
- Run the application using Docker Compose:
```
make start
```
Verify the Application:
- Check all containers are in Running state:
```
docker ps
```
Access the Application:
- 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”.
Run a Predefined Pipeline:
- Run the following commands to start the pipeline
```
./run_sample.sh
```
- Expected Results:
- The dashboard displays detected cars.

Stop the Containers#

To stop the application, use the following commands:
```
make stop
```

Next Steps#

How to Customize the Application

Troubleshooting#

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
```