Overview
Implements a reference pipeline using libraries for content analysis of video files. Includes database ingestion, content search and visualization as a foundation.
Select Configure & Download to download the sample and the software listed below.
- Time to Complete: 20 minutes
- Programming Language: Python*, JavaScript*, Shell*, C++
- Available Software:
- GStreamer
- OpenCV
- NGINX
- Kafka
- Zookeeper
- Intel® Distribution of OpenVINO™ toolkit
Target System Requirements
- 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.
Software Stacks
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.
Get Started
Prerequisites
Follow the steps on GitHub to install the prerequisites.
Install the Sample
Select Configure & Download to download the sample.
Build the Sample
Follow the steps on GitHub to build and run the sample.
Learn More
To continue learning, see the following guides and software resources:
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