Launched at AWS re:Invent 2018, Amazon Sagemaker Ground Truth is a capability of Amazon SageMaker that makes it easy to annotate machine learning datasets. Customers can efficiently and accurately label image, text and 3D point cloud data with built-in workflows, or any other type of data with custom workflows. Data samples are automatically distributed to a workforce (private, 3rd party or MTurk), and annotations are stored in Amazon Simple Storage Service (S3). Optionally, automated data labeling may also be enabled, reducing both the amount of time required to label the dataset, and the associated costs.
As models become more sophisticated, AWS customers are increasingly applying machine learning prediction to video content. Autonomous driving is perhaps the most well-known use case, as safety demands that road condition and moving objects be correctly detected and tracked in real-time. Video prediction is also a popular application in Sports, tracking players or racing vehicles to compute all kinds of statistics that fans are so fond of. Healthcare organizations also use video prediction to identify and track anatomical objects in medical videos. Manufacturing companies do the same to track objects on the assembly line, parcels for logistics, and more. The list goes on, and amazing applications keep popping up in many different industries.
Of course, this requires building and labeling video datasets, where objects of interest need to be labeled manually. At 30 frames per second, one minute of video translates to 1,800 individual images, so the amount of work can quickly become overwhelming. In addition, specific tools have to be built to label images, manage workflows, and so on. All this work takes valuable time and resources away from an organization’s core business.
AWS customers have asked us for a better solution, and today I’m very happy to announce that Amazon Sagemaker Ground Truth now supports video labeling.
Customer use case: the National Football League
The National Football League (NFL) has already put this new feature to work. Says Jennifer Langton, SVP of Player Health and Innovation, NFL: “At the National Football League (NFL), we continue to look for new ways to use machine learning (ML) to help our fans, broadcasters, coaches, and teams benefit from deeper insights. Building these capabilities requires large amounts of accurately labeled training data. Amazon SageMaker Ground Truth was truly a force multiplier in accelerating our project timelines. We leveraged the new video object tracking workflow in addition to other existing computer vision (CV) labeling workflows to develop labels for training a computer vision system that tracks all 22 players as they move on the field during plays. Amazon SageMaker Ground Truth reduced the timeline for developing a high quality labeling dataset by more than 80%”.
Courtesy of the NFL, here are a couple of predicted frames, showing helmet detection in a Seattle Seahawks video. This particular video has 353 frames. This first picture is frame #100.
This second picture is frame #110.
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