Implements a reference pipeline using libraries for content analysis of video files. Includes database ingestion, content search and visualization as a foundation.
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- Time to Complete: 20 minutes
- Available Software:
- Intel® Distribution of OpenVINO™ toolkit
- Intel® Xeon® platform, 64 GB RAM or higher
- Recommended OS: Ubuntu* 18.04 / CentOS* 7
- Disk Space needed: 3.5 GB (Source: 1 GB, Docker* Images: 2.5 GB)
How It Works
This sample implements libraries of video files content analysis, database ingestion, content search and visualization:
- Ingest: Analyze video content and ingest the data into the VDMS.
- Visual Data Management System (VDMS): Store metadata efficiently in a graph-based database.
- Visualization: Visualize content search based on video metadata.
The sample is powered by the following Open Visual Cloud software stacks:
- Media Analytics: The GStreamer-based media analytics stack is used for object, face and emotion detection. The software stack is optimized for Intel® Xeon® Scalable processors.
- NGINX Web Service: The NGINX/FFmpeg-based web serving stack is used to store and segment video content and serve web services. The software stack is optimized for Intel® Xeon® Scalable processors.
Follow the steps on GitHub to install the prerequisites.
Install the Sample
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Build the Sample
Follow the steps on GitHub to build and run the sample.
To continue learning, see the following guides and software resources:
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.