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
