Synamedia is a leading video technology provider addressing the needs for premium video service providers and direct-to-consumer (D2C) with a comprehensive solution portfolio. Synamedia solutions spread across several pillars such as video networks, TV platforms, advertisement and monetization, and content protection and piracy disruption.
Synamedia partnered with AWS to use artificial intelligence (AI) to develop enhanced video search capabilities for long-form video. This is to assist their customers in searching for videos based on a description of scenes that aren’t described in the metadata of the assets. For example, searching for a video (even within a series) that contained a scene on a boat that isn’t significant enough to be mentioned in the metadata. This enables content discovery driven from real-world objects.
With Amazon Rekognition Video, Synamedia built an AI solution that was able to perform label detection in videos and in images using standard and custom models. This enabled scene-level detection of specific objects in long-form video, based on what is actually in the scene at the time. This new capability allows users to find specific occurrences within the long-form video, based only on a general description of what they’re looking for. This enables Synamedia to perform extremely fast when onboarding new content, which now takes a few hours to spin up and get results. The solution is simple to use and extensive by providing the ability to add further custom models for domain-specific images.
Using AI to index visual content
As both supply of video content and demand for greater video insights continue to grow, effective video search capabilities are becoming more important. Traditional video search, however, is typically limited to basic information such as the video title, or in some instances, to metadata attached as tags that describe the key themes or content of the video.
Most descriptive information needs to be added manually, but this becomes prohibitive as the quantity of video grows. As a result, traditional video search performance is often limited. This limitation is even more pronounced for long-form video content, for which scene-level metadata usually doesn’t exist, given how expensive and time-consuming it is to produce.
To address this limitation, Synamedia set out to develop an AI-powered video search solution using computer vision to automatically identify scene-level details in any given video, and make that information discoverable to users based on general descriptions of those scenes.
Using Amazon Rekognition to build a custom computer vision solution in just 2 weeks
To accomplish this goal, Synamedia’s Software Engineering Fellow, Avi Fruchter, turned to Amazon Rekognition, a fully managed video analysis service that helps accelerate the process of using computer vision models to detect relevant scene-level occurrences such as objects, activities, and even text and scenes.
Amazon Rekognition Video accelerates the development of computer vision solutions for video by automatically processing and tagging video content using computer vision models. These models are fully managed and maintained by Amazon Rekognition. It removes the undifferentiated heavy lifting of managing the necessary infrastructure, and also reduces the technical expertise required to build and deploy these models.
To get started, you simply choose which of Amazon Rekognition’s wide range of capabilities is relevant to your task, and call the relevant API. The results are then returned as an easy-to-manage JSON response for each job.
For example, Synamedia used the StartLabelDetection API to automatically generate a list of labels for objects detected in each video frame of their video library. From this simple API call, Amazon Rekognition returned the list of labels, the confidence score of each, and the relevant timestamps for each frame. This enabled Synamedia to immediately create an entirely new set of search metadata for each video in their test library. Users are then able to search for specific video content just by describing specific objects or scenery they’re interested in, and get results that not only match their query, but that also point them to the specific scene in the video that featured that content.
Other relevant Amazon Rekognition APIs for video analysis are StartFaceDetection, StartPersonTracking, and StartSegmentDetection—a feature that can identify the moment that scenes in a video change.
Amazon Rekognition works on both pre-recorded and live video. Pre-recorded video is read from Amazon Simple Storage Service (Amazon S3), and live video can be processed from Amazon Kinesis Video Streams.
Synamedia chose Amazon Rekogntion for its ability to rapidly expand their capabilities. Synamedia’s innovation team is dedicated solely to building new technical innovations in video and has strong technical expertise. However, even for them it’s not always possible to have deep domain expertise in all areas of video technology. Enter Amazon Rekogntion, which extended their capabilities in computer vision, enabling them to conceptualize a use case and quickly test its viability.
“It was extremely fast to onboard, and the results were extremely quick,” Avi Fruchter says. “We are not always domain experts in all areas of ML, and Amazon Rekognition gives us the ability to leverage our existing expertise into new types of enhanced use cases for our customers.”
Synamedia anticipates their solution will have broad benefits for a wide range of customers, including companies with large video libraries as well as the growing number of companies who need to monitor specific events in live video feeds, such as health and safety risks.
With Amazon Rekognition Video, Synamedia was able to build and test an advanced video search capability in a matter of weeks, without needing to hire or develop additional specialized computer vision expertise.
This new capability has enabled Synamedia to expand the impact of its innovation team and continue with its mission to drive new video innovation for its customers.
About the authors
Daniel Burke is the European lead for AI and ML in the Private Equity group at AWS. Daniel works directly with Private Equity funds and their portfolio companies, helping them accelerate their AI and ML adoption to improve innovation and increase enterprise value.
John Shaw is the North American lead for AI and ML in the Private Equity group at AWS. John works directly with Private Equity funds and their portfolio companies, helping them accelerate their AI and ML adoption to improve innovation and increase enterprise value.