Quick Guide to Building Real-Time Video Analytics Platform

  • Real-Time Video Analytics Engine is a Platform to utilize Advanced Image Processing Algorithms to turn video into Actionable Intelligence. Massive Scale Video Processing is the greatest challenge holding an excellent potential for Analytics.
  • Real-Time Video Analytics used for Monitoring Traffic Control, Retail Store Monitoring, Surveillance, and Security.
  • Real-Time Video Analytics Platform gives a low cost, accurate analysis of live videos using Open Source Big Data Technologies including OpenCV, Apache Kafka, Apache Spark, HDFS, Tensor Flow, and Amazon S3.

Challenges to Quality Video Analytics

  • Traditional Computer Vision System suffers from a limitation that a server along with CV library collects and process the data at the same time. A failure in the server causes loss of Streaming Video Data.
  • To detect node failure and to switch the processing to another node result in fragmentation of data.
  • Major challenge relies on running an Image Processing algorithms in a dynamic environment.
  • Thus managing and efficiently analyze the data brings challenge to build a system to eliminate the addressed problems.

Solution Offerings for Video Analytics


Perform Real-Time Video Analytics backed by open source Big Data technologies.

The system divided into three main components -

  • Video stream collector
  • Stream data buffer
  • Video stream processor

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