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Easy Live Video using AI/ML on Top of Elemental MediaLive

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With the increased usage of video streaming for work purposes, there is a strong need of monitoring of live and recorded video broadcasts. The quality checks could be as simple as signal errors to issues with subtitles, audio language, which are typically being monitored by human operators. It becomes very difficult sometimes for live broadcast.

The simple usage of artificial intelligence (AI) can automate many of the monitoring tasks which is getting done by human resources. The AI-based detections can help you to analyze the content of a HTTP Live Streaming (HLS) video stream. AWS reckognition performs an example set of monitoring checks in near real-time (<15 seconds).

Here below is an attempt to familiarize you with the technologies and standards used in this solution.

  1. HLS is an HTTP adaptive bitrate streaming communications protocol.
  2. AWS Elemental MediaLive is a real-time video service that lets you create live outputs for broadcast and streaming
  3. Amazon Rekognition Custom Labels allows you to build models to identify the objects and scenes specific to your business needs

A robust broadcast quality control solution should definitely monitor various aspects of the livestreams:

Traditional image and audio analysis algorithms can get used for some of the situation and many are better suited for detection using Machine Learning (ML) models.

 

  1. The video ingestion pipeline produces HLS streams using AWS Elemental MediaLive and is stored in Amazon Simple Storage Service (Amazon S3)
  2. A video processing pipeline orchestrated by AWS Step Functions based automation which performs monitoring checks on extracted frames and audio from every video segment

Automated Verification Outcomes:

  1. Audio silence detection – based on predefined volume threshold
  2. Logo verification – Known logos from images is well suited for Convolutional Neural Networks (CNN) based ML models. Object detection models have been created using Amazon Rekognition Custom Labels.
  3. Program type verification: Whether the video looks like the type of program it should be. To ensure this, a custom image classification model created using Amazon Rekognition Custom Labels.
  4. Character/ person identification: Whether this video is showing the correct person or actor. For verifying this, the face image extraction feature of Amazon Rekognition to look for person/ actors on screen can be leveraged along with Rekognition Custom Labels to train a model to recognize a specific show/ program.

 

In combination with Media Live and Amazon AI/ML services, you can easily broadcast the content of your dream. The near real-time AI/ML intelligence would bring in more efficiency to expect accuracy up to your need. Stay tuned for more media solutions using new-age technologies.

 

   

   

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