Amazon Rekognition Video for media analysis

A+E Networks receives thousands of hours of new programing each year, with each file going through dozens of automated workflows to get to the right people at the right time. This automation is often hampered, however, by a key challenge – identifying where each segment within the file begins or ends. Our technicians must first view the video file and then manually enter every timecode to enable automated processes like transcode and quality control. With the metadata from Amazon Rekognition Video, we now have the ability to make quick, automated decisions on content as soon as it arrives. Knowing where segments start or stop with data-informed timecodes enables earlier media supply chain decisions - like what length to make a preliminary screener that starts from the first frame after color bars or slate, eliminating slugs and ending before credits. This has the potential to help us improve the quality of our output, save hundreds of work-hours each year, and respond quickly in a highly-dynamic content marketplace.

Editors and producers in the broadcasting and creative video industry spend huge amounts of time going through large volumes of video footage to produce content. This process is monotonous, time-consuming and expensive. Promomii aims to streamline such labor-intensive work by providing accurate and thorough video analysis for our clients, so that they can allocate more resources towards creative work. By combining Amazon Rekognition Video features such as shot detection with PromoMii’s own algorithms, we can quickly and easily provide editors with the most interesting or valuable visual shots during their creative process and help them sell the content better in lesser time.

The Nomad Platform leverages video shot and segment level analysis for detecting, generating, and searching rich metadata for objects, persons, labels, dialogue and screen text. Analyzing the video and detecting the discrete shots accurately has been very challenging, and up to this point, we’ve used an in-house custom shot analyzer to separate the video into the searchable segments. With the new Amazon Rekognition Video features for media analysis, our shot detection accuracy has doubled, and we get the added benefit of detecting other segment types like black frames and end credits automatically. Higher shot detection accuracy and newly detectable segment types in the Nomad Platform allows us to greatly improve the user search experience and substantially reduce customer costs by avoiding additional metadata processing that was required previously.

Today, television channels, driven by the demands of digital consumers, need to adapt traditional, long-form content produced for linear TV into short-form segments to satisfy online consumption. Segmenting and clipping content editorially is important for broadcasters so viewers can directly access the parts that are interesting to them. The Synchronized platform automates the full workflow required to segment, clip and distribute video content for broadcasters. However, accurate, automatic transformation of audiovisual content into editorial segments is an extremely complex task requiring layers of different techniques. But now, by combining Amazon Rekognition Video with our platform’s Smart-Segmentation service, we can significantly accelerate, streamline and automate the creation and delivery of clips accurately to TV editorial teams. They can then manipulate the segments without requiring specialists, and distribute them immediately. This process is not scalable if done manually. In addition, the ability to automatically detect end credits with Amazon Rekognition Video allows us to offer our customers a fully automated, turnkey solution to add features such as “Next Episode” buttons to their content catalogs.