Amazon Rekognition Video is a machine learning powered video analysis service that detects objects, scenes, celebrities, text, activities, and any inappropriate content from your videos stored in Amazon S3. Rekognition Video also provides highly accurate facial analysis and facial search capabilities to detect, analyze, and compare faces, and helps understands the movement of people in your videos.
Each result or detection is paired with a timestamp so that you can easily create an index for detailed video search, or navigate quickly to an interesting part of the video for further analysis. For objects, faces, text, and people, Rekognition Video also returns bounding box coordinates, which is the specific location of the detection in the frame.
Amazon Rekognition Video can also monitor a live stream that you create from Amazon Kinesis Video Streams to detect and search faces from face data that you provide. You can incorporate audio analysis such as closed captioning, profanity filtering and streaming video transcription into your applications by using Amazon Transcribe along with Amazon Rekognition Video.
Using the object, scene, activity, celebrity, text and face analysis metadata generated by Amazon Rekognition Video, you can automatically index large archives of video assets, and make them easily searchable. Your operators can quickly find the assets they need, instead of manually looking through all videos. Using Amazon Rekognition Video, Amazon Transcribe, and end-to-end serverless AWS solutions such as Media2Cloud and Media Insights Engine, you can seamlessly curate, filter and monetize archives across their journey from tape to a Media Asset Management (MAM) system.
With Amazon Rekognition Video, you can quickly flag when inappropriate or brand-unsafe content appears in thousands of hours of assets. Instead of looking through every second of each asset, your human moderators only need to look at the timestamps flagged by Amazon Rekognition Video. Further, using the detailed hierarchy of moderation labels available, you can address the compliance needs of different international markets. To perform audio moderation, you can use metadata from Amazon Transcribe.
Amazon Rekognition Video enables you to serve advertisements that are most relevant to the video content shown. By using the labels, activities or celebrities detected at a certain timestamp, you can boost the effectiveness and returns of the advertisement that will be shown right after that content airs.
With Amazon Rekognition Video, you can analyze shopper behavior and density in your retail store by studying the path that each person follows. Using face analysis, you can also understand the average age ranges, gender distribution and emotions expressed by shoppers without identifying them.
Amazon Rekognition Video allows you to create applications that help find missing persons in stored or streaming videos. By searching for their faces against a database of missing persons that you provide, you can accurately flag potential matches and speed up a rescue operation.
Amazon Rekognition Video automatically identifies thousands of objects such as vehicles or pets, scenes like a city, beach, or wedding, and activities such as delivering a package or dancing. For each label detected, you get a confidence score. For common objects such as 'Person' or 'Car', you also get object bounding boxes to enable counting and object localization. Amazon Rekognition Video relies on motion in the video to accurately identify complex activities, such as “blowing out a candle” or “extinguishing fire”. Using this rich metadata, you can make your content searchable or serve advertisements that best match the context of the content preceding it.
Amazon Rekognition Video automatically detects inappropriate content such as nudity, violence or weapons in videos, and provides timestamps for each detection. You also get a hierarchical list of labels with confidence scores, describing sub-categories of unsafe content. For example, 'Graphic Female Nudity' is a sub-category of 'Explicit Nudity'. Confidence scores and detailed labels allow you to set up varied business rules to serve the compliance needs of different markets and geographies.
Amazon Rekognition Video automatically detect and read text in videos, and provides the detection confidence, location bounding box, as well as the timestamp for each text detection. In addition, you get convenient options to filter out words by regions of interest (ROIs), word bounding box size, and word confidence score. For example, you may only want to detect text in the bottom third region for on-screen graphics or only the top left corner for reading scoreboards in a soccer game.
With Amazon Rekognition Video, you can detect and recognize when and where well known persons appear in a video. The time-coded output includes the name and unique id of the celebrity, and URLs pointing to related content for the celebrity, for example, the celebrity's IMDB link.
Amazon Rekognition Video can detect up to 100 faces in a video frame, and return the bounding box location. For each detected face, you can also get additional attributes such as gender, emotions, estimated age range, and whether the person is smiling, along with timestamps for each detection.
Amazon Rekognition Video can identify known people in a video by searching against a private repository of face images. You get a similarity score for each match, and timestamps for each instance where the same person is identified during the video. Amazon Rekognition Video can also cluster all unknown people in a video who don’t have any matches in the repository, and return timestamps with unique identifiers for each such person.
With Amazon Rekognition Video you can capture where, when and how each person is moving in your video. Amazon Rekognition also provides a unique index for each person found, allowing you to count the number of people in the video.
Amazon Rekognition Video can analyze your live video streams in real time to detect and search for faces. By providing a stream from Amazon Kinesis Video Streams as an input to Rekognition Video, you can perform face search against a repository of your own images with very low latency.
CBS Corporation is a mass media company that creates and distributes industry-leading content across a variety of platforms globally. CBS owns the most-watched television network in the U.S. and one of the world’s largest libraries of entertainment content, making its brand — “the Eye” — one of the most recognized in business.
"At CBS, we place significant efforts to ensure we moderate inappropriate content within our programming as to not offend our global viewers or violate government regulations. This is supported by investments in manual methods to execute near real-time screening and editing of hundreds of hours of content every month. To scale our internal processes, we are looking to Amazon Rekognition to automate the moderation of our video content while leveraging the new feature of Custom Labels to further refine moderation models. This will enable us to automate the tagging of sensitive content such as nudity, obscene gestures, and violence, and speed up processing from hours to minutes."
Jamie Duemo, Senior Vice President, MultiPlatform Distribution - CBS Operations and Engineering
VidMob is a technology platform that connects marketers with a global network of expert editors, animators, and motion graphic designers.
“Performance data isn’t a problem, but understanding why certain creative assets work better than others, and then having the ability to act on that information is. By building VidMob’s Agile Creative Suite™ on top of Amazon Rekognition, we are addressing two of the most significant marketer pain points. To date, our Agile Creative Suite has analyzed over 40,000 creative assets with Rekognition. The granularity of the data from Rekognition allows us to surface incredible insights for our clients, and enables them to see their content in a whole new way.”
Alex Collmer, CEO & Founder - VidMob
Pattern89 is the world's first data science coaching platform for paid social.
“Pattern89 uses Amazon Rekognition to provide our customers with deep data analysis including creative coaching to improve ad performance on Facebook and Instagram. Our customers have been able to implement our recommendations to reduce their ad spend, increase revenue, and improve efficiency metrics. We chose Amazon Rekognition because of its simple API, support for multiple media types, and its best-in-class labeling and face detection.”
Matt Brown, CTO - Pattern89