GitHub project Readme

# 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. ## 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} :hidden: get-started how-it-works alert-mode object-detection-pipeline api-reference known-issues release-notes :::