How some of AWS's most innovative customers are using computer vision technologies

From counting fish to identifying touchdowns, AWS customers are utilizing computer vision and pattern recognition technologies to improve business processes and customer experiences.

Computer vision, the automatic recognition and description of images and video, has applications that are far-reaching, from identifying defects in high speed assembly lines and its use in autonomous robots, to the analysis of medical images, and the identification of products and people in social media. This week, in line with the IEEE Computer Vision and Pattern Recognition (CVPR) conference, we’ve rounded up examples of how some of AWS's most innovative customers are utilizing computer vision and pattern recognition technologies to improve business processes and customer experiences. This includes approaches such as data scientists building custom vision models using Amazon SageMaker, and application developers using Amazon Rekognition and Amazon Textract to embed computer vision into their applications.

Advertising

REA Group image
REA Group has developed an image compliance system that automatically detects any noncompliance and notifies home sellers.
fstop123/Getty Images

In advertising and other online media, computer vision can automate content moderation. REA Group, a multinational digital advertising company specializing in property and real estate, provides search-based portals that enable property sellers to upload images of properties on the market to deliver a wide, searchable selection to their consumers. REA Group discovered that images uploaded to their portal often weren’t compliant with their usage terms. Some images included trademarks or contact details of the sellers, which created lead attribution challenges. They set up a dedicated team of individuals to manually review the images for unapproved content, but the large volume of daily uploads and the additional review process delayed the property listing time by several days. The REA team developed an image compliance system that automatically detects any noncompliance and notifies sellers. To augment their existing machine learning models, they're using Amazon Rekognition Text in Image, which detects and extracts text in images, enabling them to increase the accuracy of detecting noncompliance and reduce false positives by more than 56 percent. They added business rules that factored in a variety of predictions from their own models, and from Amazon Rekognition, to enable automated decision-making.

Agriculture

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Aquabyte's machine learning algorithms can estimate how much a fish weighs while still in the water.

Agriculture has also benefited from computer vision. Fish farming is one of the most efficient sources of protein, since a pound of feed equates to nearly a pound of protein. But the cold, dark waters of fish habitats make it nearly impossible to effectively manage these farms from the surface. Historically, fish farmers have had to randomly scoop fish out of the water to measure their weight and check for disease. Aquabyte’s machine learning solution reimagines this process by using underwater cameras that keep tabs on the fish and compare photos of them over time. The machine learning algorithms, running on Amazon SageMaker, can estimate how much each fish weighs while it’s still in the water. The system can also monitor the fish for sea lice, a parasite that is a major problem in salmon farms, and the subject of significant regulation in Norway, where the bulk of Aquabyte’s client base currently operates. Without a solution like Aquabyte, managing sea lice amounts to nearly a quarter of the cost of operating a salmon farm. Aquabyte’s cameras have counted 2 million sea lice to date, the result of billions of images being captured. The Aquabyte team has been working on methods that would allow farmers to track individual fish for growth-tracking and breeding purposes. In the future, machine learning might even help automate elements of the farms by intelligently distributing fish feed, for example.

Autonomous driving

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DeepMap is focused on solving the mapping and localization challenge for autonomous vehicles.

Industries like autonomous driving wouldn’t even be possible without the help of computer vision. Perhaps you think the world is already sufficiently mapped. With the advent of satellite images and Google Street View, it seems like every square inch of the globe is represented in data. But for autonomous vehicles, much of the world is uncharted territory. That’s because the maps designed for humans “can’t be consumed by robots,” says Tom Wang, the director of engineering at DeepMap, a Palo Alto startup focused on solving the mapping and localization challenge for autonomous vehicles. According to Wang, these new kinds of vehicles need higher precision maps with richer semantics, things like the traffic signals, a lot of different traffic signs, driving boundaries, and connecting lanes. For DeepMap computer vision is critical. DeepMap needs to run a vast volume of image detections to automatically generate a comprehensive list of map features and detect dynamic road changes. Using Amazon SageMaker, DeepMap updates training models within a day and runs image detection on tens of millions of images on a daily basis to keep up with ever-changing conditions.

Education

Certipass, a UNI ISO standards accredited body for the certification of digital skills
Certipass was able to build their solution in under 30 days, enabling all their testing centers to test candidates online during the COVID-19 pandemic.
fizkes/Getty Images/iStockphoto

In the wake of the COVID-19 pandemic, many educational institutions needed to quickly pivot to the online proctoring of exams, leading to a need for new ways to verify identification. Certipass, a UNI ISO standards accredited body for the certification of digital skills, is the primary provider of the international digital competency certification –European Informatics Passport (EIPASS).

Since the EIPASS Certification is an international standard, Certipass has made it their mission to ensure maximum security, objectiveness, transparency, and fairness during the entire online evaluation process. Certipass used Amazon Rekognition for automated candidate identity verification during tests that are in line with e-Competence Framework for Information and Communication Technology (CEN) and The Digital Competence Framework for Citizens (Joint Research Centre). They were able to build the solution in under 30 days to enable all their testing centers to test candidates online during COVID-19.

Financial services

Aella Credit
Aella Credit provides easy access to credit in emerging markets using biometric, employer, and mobile phone data
Victor Karanja/Getty Images

In financial services, Aella Credit provides easy access to credit in emerging markets using biometric, employer, and mobile phone data. For those in emerging markets, identity verification and validation is one of the major challenges to accessing retail banking services. How can you know that people are who they say they are in communities that don't have proper identification systems? Aella Credit uses Amazon Rekognition to analyze images to verify a customer’s identity and give them access to financial and healthcare services with minimal friction. Amazon Rekognition helps to automate video and image analysis, with no machine learning expertise required. What would have taken days to verify someone’s identity manually, now happens in seconds. Customers can actually receive their loan in their account in less than five minutes, broadening access to credit.

Financial technology

To make sure users are getting the largest possible tax refund, Intuit incorporates machine learning throughout the TurboTax experience to help users file their taxes more efficiently. TurboTax uses machine learning to shorten the filing process, which takes an average of 13 hours.

Taxes image for AWS customer success story
TurboTax utilizes machine learning to shorten the filing process.
simpson33/Getty Images/iStockphoto

With Intuit’s computer vision capabilities supported by Amazon Textract, entering information from tax forms like W2s or 1099s takes seconds. Rather than a user having to enter form fields manually, the service scans pictures of the forms and digitizes them. Then, using contextual data from TurboTax’s existing database of tax codes and compliance forms, Amazon Textract verifies accuracy and identifies any anomalies or missing data for the user.

Healthcare

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By combining the power of machine learning and computer vision, an interdisciplinary team of researchers at Duke University has created a faster, less expensive, more reliable, and more accessible system to screen children for autism spectrum disorder.

Machine learning plays a key role in many health-related realms - from providers and payers looking to expedite the care continuum to pharma and biotech researchers looking to reduce costs and speed up the drug discovery and disease detection process. Researchers at Duke Center for Autism and Brain Development are using machine learning to screen for autism spectrum disorder (ASD) in children. It’s critically important to diagnose ASD as early in a child’s development as possible — starting treatment for ASD at an age of 18 to 24 months can increase a child’s IQ by up to 17 points—in some cases moving them into the “average” child IQ range of 90-110 (or above it)—and, in turn, significantly improving their quality of life. Currently, the wait time for children to receive a diagnosis could be well after the child’s third birthday. By combining the power of machine learning and computer vision, powered by AWS, an interdisciplinary team of researchers at Duke University have created a faster, less expensive, more reliable, and more accessible system to screen children for ASD.

Media and entertainment

Computer vision technology is helping sports organizations like the National Football League (NFL) improve the game for fans. The NFL works with AWS to develop real-time, state-of-the-art cloud technology leveraging machine learning and artificial intelligence to increase the efficiency and pace of the game.

For example, deep learning and computer vision technologies are being explored to aid game officiating including real-time football tracking. Within days, AWS and NFL scientists were able to create custom training data sets of thousands of images extracted from NFL broadcast game footage using Amazon SageMaker Ground Truth.

NFL football
Deep learning and computer vision technologies are being explored by the NFL to aid game officiating, including real-time football tracking.
CREDIT: National Foottball League

Working with the Amazon ML Solutions Lab, Amazon SageMaker and GluonCV with MXNet were used to train and optimize several state-of-the-art deep learning-based object detection models such as Faster-RCNN and Yolov3, to accurately detect the football across video frames. This led to a first-of-its-kind football tracking model that performs well in a number of complex scenarios, such as when the ball is highly occluded or is partially visible in different camera angles.

The NFL also uses computer vision to more easily and quickly search through thousands of media assets. The NFL photo team, official photographers of the NFL, has millions of photos in archive and generates 500,000 photos each season. Manually, they were able to tag 50,000 images over 18 months. By using Amazon Rekognition custom face collection, text in image, object detection, and Custom Labels, an automated machine learning object detection service, they were able to apply detailed tags for players, teams, objects, action, jerseys, location, etc. to their entire photo collection in a fraction of time it took previously. This allowed them to make these photos searchable and usable to everyone in the company in ways that weren't possible before.

For Sportradar, the global provider of sports and intelligence for the betting and media industries providing data coverage from more than 200,000 events annually, advances in computer vision are an opportunity to expand the depth of sports data offered to customers and reduce the costs of data collection through automation.

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For Sportradar, advances in computer vision are an opportunity to expand the depth of sports data offered to customers and reduce the costs of data collection through automation.
scyther5/Getty Images/iStockphoto

Sportradar is investing in computer vision research both through internal development and external partnerships to build computer vision data collection capabilities with an initial focus on tennis, soccer and snooker. Working with the Amazon ML Solutions Lab, Sportradar is exploring the application of state-of-the-art deep learning models for automated match event detection in soccer, moving beyond player and ball localization to understanding the intent of the play in terms of what is happening in the game.

To bring this technology into production as it matures, Sportradar is leveraging AWS services including Amazon SageMaker, EKS, MSK, FSx and Amazon’s broad range of GPU and CPU compute instances for its computer vision processing pipeline. This infrastructure allows Sportradar's researchers to test and validate computer vision models at scale and bring models from the lab to production with minimal effort while delivering the low latency, reliability and scalability needed for live sports betting use cases.

You can find more ways that AWS customers are innovating with computer vision here. More information about Amazon's participation at CVPR is available here.

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US, MA, Westborough
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IN, TS, Hyderabad
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GB, MLN, Edinburgh
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US, CA, Palo Alto
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US, WA, Seattle
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US, WA, Seattle
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US, WA, Bellevue
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US, WA, Seattle
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US, WA, Bellevue
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Senior Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. An Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical expertise and a passion for developing science-driven solutions in a fast-paced environment. The ideal candidate will have a solid understanding of state of the art NLP, Generative AI, LLM fine-tuning, alignment, prompt engineering, benchmarking solutions, or CV and Multi-modal models, e.g., Vision Language Models (VLM), zero-shot, few-shot, and semi-supervised learning paradigms, with the ability to apply these technologies to diverse business challenges. You will leverage your deep technical knowledge, a strong foundation in machine learning and AI, and hands-on experience in building large-scale distributed systems to deliver reliable, scalable, and high-performance products. In addition to your technical expertise, you must have excellent communication skills and the ability to influence and collaborate effectively with key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.
US, WA, Bellevue
Why this job is awesome? This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site. MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers. We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process. You will have a chance to develop the state-of-art machine learning, including deep learning and reinforcement learning models, to build targeting, recommendation, and optimization services to impact millions of Amazon customers. - Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site? - Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems? - Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? - Do you like to innovate and simplify? If yes, then you may be a great fit to join the DEX AI team. Key job responsibilities - Research and implement machine learning techniques to create scalable and effective models in Delivery Experience (DEX) systems - Solve business problems and identify business opportunities to provide the best delivery experience on all Amazon-owned sites. - Design and develop highly innovative machine learning and deep learning models for big data. - Build state-of-art ranking and recommendations models and apply to Amazon search engine. - Analyze and understand large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation