Video Curation Sample Reference Implementation

Published: 12/02/2021  

Last Updated: 01/13/2022

Overview

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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Video curation sample video

  • 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:

Video Curation Sample Architecture Diagram
Figure 1: Architecture Diagram

 


Get Started

Prerequisites

Follow the steps on GitHub to install the prerequisites.

Install the Sample

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Configure & Download 

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: 

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.