# Real-Time Scene Understanding
**Real-Time Scene Understanding** deploys AI-powered live video analysis for RTSP streams with FFMpeg pythonic bindings, PyAV and OpenVINO Vision Language Models (VLMs). You can process streaming video, run prompt-driven scene understanding, and monitor both system and inference performance from a web dashboard.
The key features are:
**VLM Model Support**: Configure separate alert and deep-analysis OpenVINO VLMs through environment settings, with model files mounted from `ov_models/`.
**Real-Time Streaming**: WebRTC-based low-latency video preview for live monitoring workflows.
**Prompt-Driven Scene Intelligence**: Run natural-language prompts against live frames for flexible scene interpretation.
**Performance Metrics**: Live charts for CPU/GPU/RAM plus inference metrics such as TTFT, TPOT, and throughput.
**Modular Architecture**: Containerized application, streaming, metrics, and object-storage services.
**Alert Mode**: Alert-oriented output styling for binary prompt responses (for example, "Yes"/"No").
## Use Cases
**Real-Time Monitoring**: Analyze security cameras, industrial lines, and public infrastructure with prompt-driven scene understanding.
**Operational Safety**: Detect safety-relevant conditions through targeted prompts in manufacturing, warehousing, and logistics environments.
**Accessibility and Summarization**: Generate concise, contextual scene descriptions to improve accessibility and operator awareness.
**Performance Benchmarking**: Compare VLM behavior across Intel hardware by tracking latency, throughput, and resource utilization.
:::{toctree}
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get-started.md
Quick Start
how-it-works.md
api-reference.md
troubleshooting.md
Release Notes
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