Blake  Kulas

Blake Kulas

1594392540

New – Label Videos with Amazon SageMaker Ground Truth | Amazon Web Services

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.

Object tracking

This second picture is frame #110.

Object tracking

#amazon sagemaker ground truth #announcements #artificial intelligence

What is GEEK

Buddha Community

New – Label Videos with Amazon SageMaker Ground Truth | Amazon Web Services
Blake  Kulas

Blake Kulas

1594392540

New – Label Videos with Amazon SageMaker Ground Truth | Amazon Web Services

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.

Object tracking

This second picture is frame #110.

Object tracking

#amazon sagemaker ground truth #announcements #artificial intelligence

Ajay Kapoor

1626759008

AWS Development Company | Amazon Web Development Services

PixelCrayons provides its clients with best-in-class AWS development services in India. They are backed by a team of 500+ professionals and help to reduce the operational overhead and risk of the organisations.

AWS development services aid in automating simple activities, such as to request change, monitoring, patch management, security, and backup services. Our services are efficient to provide the full-lifecycle services to establish, run, plus support IT infrastructure.

Overview of Our Amazon Web Services
PixelCrayons AWS Managed Services relieves you from infrastructure operations to provide direct access to resources toward distinguishing your business.

Ready to Get Started?
Stay ahead of competition with our professional, tailor-made & enterprise-grade Amazon Web Services. We provide you the right talent with right skills to the right business. Our professionals have expertise with modern technologies to address critical needs of global clients across industries.

Amazon web development services

#aws development services #aws development services in india #amazon web services #aws managed services #amazon web development services

Teresa  Jerde

Teresa Jerde

1596657240

New – Label 3D Point Clouds with Amazon SageMaker Ground Truth

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 and text 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.

About a year ago, I met with Automotive customers who expressed interest in labeling 3-dimensional (3D) datasets for autonomous driving. Captured by LIDAR sensors, these datasets are particularly large and complex. Data is stored in frames that typically contain 50,000 to 5 million points, and can weigh up to hundreds of Megabytes each. Frames are either stored individually, or in sequences that make it easier to track moving objects.

As you can imagine, labeling these datasets is extremely time-consuming, as workers need to navigate complex 3D scenes and annotate many different object classes. This often requires building and managing very complex tools. Always looking to help customers build simpler and more efficient workflows, the Ground Truth team gathered more feedback, and got to work.

Today, I’m extremely happy to announce that you can use Amazon Sagemaker Ground Truth to label 3D point clouds using a built-in editor, and state-of-the-art assistive labeling features.

#amazon sagemaker ground truth #announcements #artificial intelligence #news #sagemaker

Rahim Makhani

Rahim Makhani

1621483980

Get the best web app for your Business FUTURE

The web app is application software that runs on the webserver. You can easily use the web app by searching it in the web browser through Google or any other search engine, or you can also add shortcuts of the web app to your smartphone.

Web app for your business helps you to reach new customers and enables them to know about your firm and the services you provide and can know about your organization’s feedback and rating. It can also help you with the advertisement of your app among all.

Do you want to develop a web app for your business? Then it would help if you collaborated with Nevina Infotech, which is the best web application development company that will help you develop a unique web app with the help of its dedicated developers.

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Rahim Makhani

Rahim Makhani

1626238039

Find the best web app development company for your Startup

A web app is the best way to promote their business for startups. You can’t verbally go and tell everyone to visit your company, but your website or web app can do that. A web app can represent your company, and the visitors who are visiting your website or web app will get knowledge about your firm. Doing this can help you to increase your customer rate.

Nevina Infotech is the best web app development company to choose for developing your web app for your startup. We have a great team of web developers to work with. Our developers are dedicated and enthusiastic in their work.

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