Robust Platform for Live and VOD Workflows using Azure Video Indexer

Introduction

Imagine the time your staff invests in transcribing and adding metadata tags to videos. As the volume of new content increases, your staff needs more time to tag it. One person needs at least one hour to process a one-hour video, right? And if you add more people to a project, the potential for human error increases, such as typos, inconsistent taxonomies, different labels, etc. But with an Azure Video Indexer solution that uses artificial intelligence (AI), you can automate how you manage and index your media.

With an AI-powered solution, you can automate and enhance your ability to tag your content library with rich metadata, helping you discover your content faster and more efficiently. It also helps reveal how your content relates to other items in your growing library. You can also use AI to automatically generate transcriptions and translations for your media, improving the reach of your content to a wider global audience and making it easier to meet accessibility regulations.

What is Azure Video Indexer?

Azure Video Indexer is one of many modular services in the Azure Media Services family. Azure Media Services is a robust platform for live and VOD workflows. Media Services provides all the building blocks you need to build your video-based solution service, including services for encoding, packaging, indexing, distributing, and protecting your content. Azure Video Indexer uses a set of ML algorithms that are already built-in for automatic metadata extraction and eliminate the need for many (many) hours of manual tagging. You can use Azure Video Indexer with other services in Media Services or stand-alone through its API and portal.

How Azure Video Indexer works?

Azure Video Indexer includes a number of features, such as facial recognition, general sentiment analysis, transcripts, transcript translations, and content moderation. For example, Azure Video Indexer can trigger adult scenes and profanity in your media content. This only scratches the surface of Azure Video Indexer’s value by bringing all of these statistics together for you within a single service while providing predictable pricing. Azure Video Indexer detected objects, people, faces, animated characters, keyframes, and translations or transcriptions in at least 60 languages.

Azure Video Indexer uses a portfolio of Microsoft AI algorithms to analyze, categorize, and index video footage when processing files. The resulting reports are archived and can be comprehensively accessed, shared, and reused. For example, a news outlet might thoroughly search for insights about the Empire State Building and reuse its findings in various films, trailers, or promotions.

Azure Video Indexer Key Features

• Object detection: The ability to identify and find objects in an image or video. For example, a table, a chair, or a window.
• Deep search: The ability to retrieve only relevant video and audio files from the video library by searching for specific terms in the extracted reports.
• Insight: Information and knowledge derived from the processing and analyzing video and audio files that generate different insights and may include detected objects, people, faces, animated characters, keyframes, and translations or transcriptions.
• Named entities: Extract brands, places, and people from speech and visual text through natural language processing (NLP). 
• Face recognition: Image analysis to identify faces that appear in images. This process is implemented through the Azure Cognitive Services Face API.
• Labels: Identification of visual objects and actions appearing in the frame, for example, identifying an object such as a dog or an action such as running.
• Language: The language identified in the video.
• Optical Character Recognition (OCR): A process that converts images or PDF files into editable text files.
• Topics: Main topics of the video, sorted by category.
• Voice Tonality Analysis: An acoustic voice spectrogram that detects the speaker’s emotions. For example, happiness, sadness, excitement, or fear in the speaker’s voice.

Conclusion

Now that you understand the value of Azure Video Indexer, you can test the solution with your content in minutes. You may want to start customizing content models relevant to your organization. As of this document release, Video Indexer supports customizing content models for tags, languages, and people. All three of these types of content models support customization through APIs. Azure Video Indexer is a powerful ally that immediately gives you valuable insights into your media library. When you’re ready, you can extend Azure Video Indexer through integrations.

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