# Live Video Captioning
**Live Video Captioning** deploys AI-powered captioning for live video streams with Deep Learning Streamer (DL Streamer) and OpenVINO™ Vision Language Models. You can process RTSP streams, generate real-time captions, and monitor performance metrics on a dashboard.
The key features are:
**Multi-Model Support**: Switch between VLMs (InternVL2, Gemma-3, etc.) with automatic model discovery from `ov_models/`.
**Real-time Streaming**: WebRTC-based low-latency preview video delivery.
**Performance Metrics**: Live charts for CPU/GPU/RAM and inference metrics such as TTFT, TPOT, and throughput.
**Modular Architecture**: Containerized services with clearly separated backend, frontend, and pipeline configuration.
**Alert Mode**: Optional alert styling for binary classification prompts (“Yes”/“No”).
**Object-Detection-Model Support**: Optionally integrate YOLO-based detection models into the pipeline to enable object detection and frame filtering.
**Helm Deployment**: Deploy the full stack on Kubernetes with the bundled Helm chart and configurable override values.
## Use Cases
**Real-time Video Analytics**: Monitor security cameras, industrial equipment, or public spaces with AI-powered scene understanding and automatic captioning.
**Accessibility Enhancement**: Generate live captions for video content, making streams accessible to users with hearing impairments.
**Performance Benchmarking**: Evaluate VLM performance on Intel® hardware by comparing throughput, latency, and resource utilization across different models and pipeline configurations.
**Intelligent Surveillance**: Deploy custom prompts (for example, “Is there a person in the frame?”) for security and safety monitoring workflows.
:::{toctree}
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get-started.md
how-to-guides.md
how-it-works.md
api-reference.md
known-issues.md
Release Notes
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