Video AI in the Cloud: 6 Platforms and APIs



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Video AI in the Cloud: 6 Platforms and APIs

An overview of the product functionalities of some of the leading players in this emerging space.

Source: Pixabay

Artificial intelligence (AI) is increasingly being used to manage video content. Deep learning-based computer vision techniques can help recognize concepts and faces in video streams, categorize videos, automatically add captions, and enhance videos and images using techniques like super-resolution.

Developing video AI from scratch is a huge investment. Today you can leverage video AI capabilities out of the box, using the powerful video APIs offered by a number of cloud platforms.

In this article I’ll describe a few of the world’s most advanced video AI platforms:

1. Video AI on AWS

The core AI services AWS provides for video are:

Amazon Rekognition

This service makes it easy to add image and video analytics to your applications using mature, highly scalable deep learning technology. The service does not require machine learning expertise. It lets you:

  • Identify objects, people, text, and scenes in images and video
  • Detect inappropriate content
  • Perform accurate face analysis and face detection — useful for user identification, demographic and public safety scenarios
  • Amazon Kinesis Video Streaming

This service lets you securely stream videos from connected devices to AWS for analytics, machine learning (ML) analysis, and playback.

Kinesis Video Streams automatically sets up and scales all the infrastructure needed to capture streaming video data from millions of devices. It permanently stores, encrypts, and indexes video data for access through an easy-to-use API.

Kinesis Video Streams enables real-time and on-demand video streaming, and lets you perform AI-based video analysis video Amazon Rekognition, and open source frameworks like Apache MxNet, TensorFlow, and OpenCV.

2. Video AI on Microsoft Azure

Microsoft Cognitive Services, a service offered on the Azure cloud, includes the Vision package that enables video image analysis. The Vision package provides the following capabilities:

  • Computer vision — can recognize objects, typed and written texts, actions (like walking), and can identify the dominant colors of images.
  • Content moderator — can detect inappropriate content in texts, videos, and images.
  • Face API — can detect faces and group them, as well as recognize the age, genders, emotions, poses, and facial hair of the face.
  • Emotion API — a face recognition tool that can recognize and describe facial expressions.
  • Custom Vision Service — lets you build customized image recognition models with your own data.
  • Video indexer — a tool that can help you find people in videos, as well as detect the sentiment of speech and mark certain keywords.

In the Azure cloud, you typically store data using the elastic blob storage service. However, for demanding applications and real-time AI processing, it is sometimes preferable to use Azure premium storage.

3. Video AI on Google Cloud Platform

Google Cloud Platform provides services and APIs that allow you to perform AI-based operations on video streams and video files.

Video Intelligence API

Provides a pre-trained machine learning model that can automatically identify a large number of objects, locations, and actions in stored and streamed videos. It works out-of-the-box, offers high performance in common use cases, and is constantly being updated and re-trained with new objects and concepts.

AutoML Video Intelligence

Google AutoML Video Intelligence provides a graphical interface, allowing users with minimal machine learning experience to train custom models, in order to to classify and track objects in a video. The solution is suitable for projects that require labels not covered by the pretrained Video Intelligence API.

4. Video AI with IBM Watson

IBM Watson Media is an AI platform for media workflows and video processing. Its Video Enrichment product offers Computer Vision solutions for video data.

You can stream events, viewings, video marketing product launches, and OTT streams with IBM Watson Media. Video Enrichment lets you optimize video quality, perform automated video search and create captions automatically. The solution is used by educators and media companies to improve video workflows and monetize content.

5. Video AI with Pixop

PIxop Platform is a web application that helps you store, transcode and process video files in the cloud, leveraging machine learning.

Pixop provides a wide range of features, including quality analysis for videos, a project-based video asset management module, and several features that enable collaboration between teams and customers.

The Pixop platform is fully cloud-based, requiring no investment in hardware, and no need to install software. Here are several notable features of Pixop:

  • Pixop Deep Restoration — helps restore the quality of videos by performing tasks like de-blurring, eliminating compression artifacts, and injecting details into the degraded video.
  • Pixop Super Resolution — this is a transparent upscaler that helps sharpen and increase the resolution, providing more accurate results than interpolation.
  • Pixop Denoiser — helps reduce digital noise and improves grainy footage.
  • Pixop Deinterlacer — helps reconstruct the details of interlaced videos and turn it into a non-interlaced and progressive form.
  • Pixop Dejitterer — helps stabilize and repair scan lines that were displaced due to the conversion of the video to a digital format.

6. Video AI with Valossa

Valossa AI is a technology platform that provides analytics and automated profiling for video content. It provides the following main features:

  • Auto preview — automatically generates video previews to speed up content marketing and promotional activities. Can be used to create video-on-demand services with smart previews of videos delivered online.
  • Video recognition API — able to detect and describe the key concepts in a video stream. Generates scene-level, time-coded metadata that enablers search, retrieval, and organization of video content.
  • Face analysis toolkit — recognizes human faces in video content in real time. Analyzes real-time behavioral and demographic properties. This enables interactive applications by applying AI with real-time camera feeds.

Conclusion

Cloud-based video AI platforms offer amazing capabilities, which you can easily leverage even if your team does not have data science skills. By using a video AI platform you can achieve some or all of these goals:

  • Automated pre-processing of video streams
  • Automatically optimize video quality
  • Recognizing objects, text, and concepts in videos
  • Automatically add captions, tags and categories to videos
  • Create previews and short versions of videos

I hope this will be helpful as you build the next generation of your video offering.

AI/ML

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