Automating broadcast video monitoring using machine learning - blog post and sample application
In the M&E industry, monitoring live broadcast and OTT video streams has largely been a manual process relying on human operators constantly watching the stream to identify quality or content issues. Latest advances in artificial intelligence(AI) can help automate many monitoring tasks that was once manual and support monitoring at greater scale. This repo presents a demo application for realtime livestream monitoring using AWS serverless and AI/ML services.Architecture
The solution architecture for the application consists of three main components:- A video ingestion pipeline where HLS streams produced by AWS Elemental MediaLive is stored in an Amazon S3 bucket
- A video processing pipeline orchestrated by AWS Step Functions that performs monitoring checks on extracted frames and audio from each video segment
- A web application that demonstrates the realtime status and details of each monitoring check being performed on the video stream
Github Source:
