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Get Started

The Live Video Captioning sample application demonstrates real-time video captioning using Intel® DLStreamer and OpenVINO™. It processes RTSP video stream, applies video analytics pipelines for efficient decoding and inference, and leverages a Vision-Language Model(VLM) to generate live captions for the video content. In addition to captioning, the application provides performance metrics such as throughput and latency, enabling developers to evaluate and optimize end-to-end system performance for real-time scenarios.

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 the application: Execute the application to see real-time captioning from your video stream.
  • Modify application parameters: Customize settings like inference models and VLM parameters to adapt the application to your specific requirements.

Prerequisites

  • Verify that your system meets the minimum requirements. See System Requirements for details.
  • Install Docker: Installation Guide.
  • Install Docker Compose: Installation Guide.
  • RTSP stream source (live camera or test feed). Please refer to this guide to create simulated RTSP test feed stram using exisiting video files.
  • OpenVINO-compatible VLM in ov_models/. User may use the script provided to prepare the model.
  • OpenVINO-compatible Object Detection Models in ov_detection_models/. Only required when user wish to enable object detection in the pipeline. Please refer to this guide to enable object detection in the pipeline.

Running the application

  1. Clone the repository:

    # Clone the latest on mainline
git clone https://github.com/open-edge-platform/edge-ai-suites.git edge-ai-suites
# Alternatively, clone a specific release branch
git clone https://github.com/open-edge-platform/edge-ai-suites.git edges-ai-suites -b <release-tag>

    Note: Adjust the repo link appropriately in case of forked repo.

  2. Navigate to the Directory:

    cd edge-ai-suites/metro-ai-suite/live-video-analysis/live-video-captioning

  3. Configure Image Registry and Tag:

       export REGISTRY="intel/"
   export TAG="latest"

    Skip this step if you prefer to build the sample applciation from source. For detailed instructions, refer to How to Build from Source guide for details.

  4. Configure Environment: Create a .env file in the repository root:

    WHIP_SERVER_IP=mediamtx
WHIP_SERVER_PORT=8889
WHIP_SERVER_TIMEOUT=30s
PROJECT_NAME=live-captioning
HOST_IP=<HOST_IP>
EVAM_HOST_PORT=8040
EVAM_PORT=8080
DASHBOARD_PORT=4173
WEBRTC_PEER_ID=stream
WEBRTC_BITRATE=5000
ALERT_MODE=False
ENABLE_DETECTION_PIPELINE=False

    Notes:

    • HOST_IP must be reachable by the browser client for WebRTC signaling.
    • PIPELINE_SERVER_URL defaults to http://dlstreamer-pipeline-server:8080.
    • WEBRTC_BITRATE controls the video bitrate in kbps for WebRTC streaming (default: 2048).

  5. Download/Export Models: Run the following scripts to download and convert VLM models.

    chmod +x download_models.sh
./download_models.sh [internvl2_1B|gemma3|internvl2_2B]

    For other OpenVINO supported models, provide the HuggingFace model name.

    ./download_models.sh OpenGVLab/InternVL2_5-1B

    For gated models, please export you HF_TOKEN before running the scripts above:

    export HF_TOKEN=<YOUR_HUGGING_FACE_TOKEN>

  6. Start the Application: Start the application using Docker Compose tool:

    docker compose up

  7. Access the Application:

    To start processing video with live captioning:

    1. Open the dashboard at http://<HOST_IP>:4173.
    2. Enter an RTSP URL for your video stream.
    3. Select a VLM model from the dropdown.
    4. Customize the prompt and maximum tokens as needed.
    5. Click Start to begin captioning.

    Note: If running in a proxy network, ensure that your RTSP stream URLs or IPs are added to the no_proxy environment variable to allow direct connections to the stream source without going through the proxy.

  8. Stop the Services: Stop the sample application services using below:

    docker compose down

Advanced Setup Options

For alternative ways to setup the application, see:

Testing

The project uses pytest for unit testing. Tests are located in the tests/ directory under the app/ folder.

Install Test Dependencies

cd app
uv sync --group test

Run All Tests

uv run pytest

Run a Specific Test File

uv run pytest tests/test_routes_runs.py

Run Tests with Coverage Report

uv run pytest --cov=backend --cov=main --cov-report=term-missing

Generate an HTML Coverage Report

uv run pytest --cov=backend --cov=main --cov-report=html

Open htmlcov/index.html in a browser to view the detailed coverage report.

Supporting Resources