Prime Video's work on sports field registration, recap/intro detection

Two papers at WACV propose neural models for enhancing video-streaming experiences.

Like all of Amazon’s major technology groups, Amazon Prime Video has a dedicated team of scientists who are working constantly to find new ways to delight our customers and improve our products.

Our work was on display at this year’s IEEE Winter Conference on Applications of Computer Vision, where we presented two papers. One was on sports field registration, or understanding the spatial relationships between objects depicted in sports videos. The other was on recap and intro detection, or automatically identifying the recaps and intros at the beginnings of TV shows, so viewers can skip them if they want.

American football, with dense features
At top is video of an American football play; bottom left is a visualization of our grid keypoints; bottom right is a visualization of our dense features.

Sports field registration involves mapping video images onto a topographical model of the field, to enable enhancement of the video feed. It’s the technology behind the virtual first-down lines in American-football broadcasts or the virtual world-record lines in swimming broadcasts.

Usually, sports field registration requires onsite cameras equipped with sensors and calibrated to reference points on the field. Combining the sensor output with the cameras’ video yields very accurate field registration.

We address the problem of sports field registration in the absence of instrumentation, using video from a single camera capable of pan, tilt, and zoom (PTZ) motion. This could enable the addition of cutting-edge graphics to broadcasts of minor-league or amateur sporting events, broadcasts of less-popular sports, or even video signals from uninstrumented secondary cameras at major sporting events.

Where previous work on this problem modeled field topography using only a few keypoints — usually, intersections of lines laid down on the field — we model the field using a dense grid of keypoints.

Model of a soccer field with a dense grid of keypoints
A traditional model of a soccer field (left), with a few keypoints at the intersections of lines, and our model (right), with a dense grid of keypoints.

Using video annotated according to our modeling scheme, we train a neural network to correlate image pixels with specific keypoints in our model of the field.

The dense grid increases the precision of our registration — provided that we correctly identify the keypoints. But of course, keypoints that don’t lie at the intersections of field lines are harder to identify.

Consequently, we use a second source of information to improve our mapping. This is a set of dense field features that represent the standard distances between lines on the field and between other identifiable regions of the field.

In the figure below, for instance, the black-and-white model at left illustrates the lines of an American-football field, while the black-and-white model at right illustrates the numbers marking the yard lines.

Maps of linear and regional features of an American football field using normalized distances between black and white pixels
An American-football field (top); maps of linear and regional features of the field (second row); and representations of those features using only the distance from each black pixel to the nearest white pixel in the feature map.

The glowing green elements of the bottom images are meant to indicate that features of the black-and-white models are being represented, not according to their absolute location on the field, but according to normalized distances between black pixels and white pixels. 

That is, whereas the keypoints represent absolute field positions, the dense feature set represents field position relative to recurring visual elements of the field. It’s thus a complementary feature set that improves the mapping between a video frame and the sports field.

Using the dense features to verify keypoints adds computational overhead, however, and our system needs to work in real time. Our network architecture therefore incorporates several properties meant to reduce this overhead.

The first is that it is a multitask network: from the input data, it produces a single vector representation that passes to both the keypoint estimator and the dense-feature extractor.

Model of an encoder passing a vector representation of input data to a keypoint detector and a dense-feature extractor
Our network architecture. A shared encoder produces a vector representation of the input data that passes to both the keypoint detector and the dense-feature extractor.

The second is that the network uses the dense features for verification only if it has reason to believe that the keypoint estimates are inaccurate. Specifically, given the initial keypoint estimate for a frame of video, the network takes several different samples of keypoints and determines whether they align with each other. If they don’t, it uses the dense features to refine its estimate (the self-verification and online-refinement modules in the diagram above).

By combining these techniques, we were able to get our sports field registration system to work in real time. In tests, we compared it to multiple state-of-the-art sports field registration systems on five data sets: soccer, American football, ice hockey, basketball, and tennis.

On different sports, our system’s performance ranged from comparable to baseline to much better. For American football, for instance, according to the standard version of the intersection-over-union measure, our system was 2.5 times as accurate as the best-performing baseline.

Five sports
At left are grid keypoints and the projection of field templates onto the videos of five different sports; at right are mappings of the camera’s field of view onto models of the fields.

Intro and recap detection

Fans of Prime Video’s hit shows, such as The Marvelous Mrs. Maisel, are familiar with the option of skipping the introductions — which usually feature credits and theme music — and recaps — quick summaries of the narrative to date — at the beginning of individual episodes.

With existing content, however, providing the option to skip intros and recaps requires hand coding. We’d like to extend that option to other Prime Video programming through automatic detection of intros and recaps.

Both intros and recaps have distinguishing features that should make them detectable. Intros tend to involve text (credits) superimposed on the screen, often with extended musical performances in the background, while recaps usually involve unusually quick cuts between scenes. Frequently, they’re also introduced by text.

Our detector is a neural network, with an architecture chosen to maximize responsiveness to these elements of intros and recaps. Unlike alternative approaches that require an entire video series to find intro and recap timestamps, our approach can work on each episode independently, which makes it more practical.

With our system, a given frame of video passes first to a convolutional neural network (CNN). CNNs are designed to step through input images, applying the same filters to successive blocks of pixels. They can thus learn to identify text regardless of what region of the screen it falls in. We also pass the input audio to the same CNN, which learns a fused representation of audio and video.

Architecture of intro and recap detector: individual frames of input video and outputs of the conditional random field
The architecture of our intro and recap detector. The blue lines at the bottom represent individual frames of input video. The outputs of the conditional random field (CRF) are “R” for recap, “I” for intro, and “C” for content.

The output of the CNN then passes to a bidirectional long-short-term-memory (Bi-LSTM) network. An LSTM is a type of neural network that processes sequential inputs in order, so that each output reflects both the inputs and outputs that preceded it. A Bi-LSTM passes through the same sequence both forward and backward. This allows our network to recognize longer-term dependencies — such as the cutting rates in particular video sequences.

Finally, the output of the LSTM passes to a conditional random field, which essentially performs curve smoothing. Smoother contours within a segment of video enable clearer identification of the boundaries between segments — between, say, intros and recaps, or between either and the new content of an episode.

In tests, we compared the performance of our system to baselines that used the same CNN but different methods to process the CNN’s output: a single-layer LSTM; a two-layer LSTM; a Bi-LSTM; and a Bi-LSTM that uses Viterbi decoding, rather than a CRF, for smoothing. We find that our system dramatically outperforms all four baselines. 

Research areas

Related content

US, Virtual
The Amazon Economics Team is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, MA, Westborough
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking interns and co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it! Key job responsibilities • Identifying creative solutions for challenging research problems in robotics and computer vision • Developing software solutions to test hypotheses and demonstrate new functionality • Prototyping concepts to collect data and measure performance • Writing code and unit tests and integrating code with other software and hardware components • Utilizing Amazon Robotics and Amazon engineering tools, processes and technologies • Delivering a final presentation to managers and engineers on the successes and challenges of their internship and the business value they have contributed
US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. We’re seeking a Principal Scientist with a deep expertise in Search Science. Your responsibilities will include everything from developing and prototyping innovative machine learning, and deep learning algorithms to implementing, testing, and supporting full solutions in a production environment. We are looking for innovators who can contribute to advancing search technology on what’s scientifically possible while remaining committed to creating world-class products. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), Earth's most customer-centric company one of the world's leading internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Key job responsibilities As a hands-on leader of this team, you’ll be responsible for defining key research questions, identifying relevant data, adopting or proposing innovative machine learning solutions conducting rigorous experiments, publishing results and working with the engineering team to deploy these solutions. As a strategic leader, you will identify investment opportunities, develop long term strategies, and propose, prioritize and deliver on goals. You’ll also participate in organizational planning, hiring, mentorship and leadership development. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team Starting in 2009, the Visual Search & Augmented Reality team has thus far launched many visual search solutions on the Amazon App that use computer vision and machine learning/deep learning to help customers complete their shopping missions more easily; multiple internal teams at Amazon (devices, Kindle, Seller services, etc.) also use our libraries and APIs to deliver solutions to their own customers. We are a full stack shop, and our team capabilities cover the whole solution spectrum, ranging across applied science, large scale engineering services, product management, UX design, and mobile app development for iOS and Android.
US, MN, Minneapolis
AWS Central Economics is an interdisciplinary team on the cutting edge of economics, statistical analysis, and machine learning whose mission is to solve problems that have high risk with abnormally high returns. Our team leverages the strengths of our scientists to build solutions for some of the toughest business problems here at Amazon AWS. We are looking for an exceptionally talented, seasoned, and motivated Economist to manage a team of economists and data scientists to drive the science for AWS. Key job responsibilities Manage a team of economists and data scientists to deliver actionable economic analyses to business leaders, provide leadership on the economics and science used in the analyses, and engage with business leaders to identify challenges AWS faces that call for in-depth economic analyses and to ensure the analyses have their intended impact.
LU, Luxembourg
&ltHire Relocation Requisition - not for posting> Provides insights to leadership on improving Supply Chain cost and Speed by using Data Science and Analytics techniques. Build Dashboards and models to industrialize these findings at scale.
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
Amazon is looking for talented Postdoctoral Scientists to join our global Science teams for a one-year, full-time research position. Postdoctoral Scientists will innovate as members of Amazon’s key global Science teams, including: AWS, Alexa AI, Alexa Shopping, Amazon Style, CoreAI, Last Mile, and Supply Chain Optimization Technologies. Postdoctoral Scientists will join one of may central, global science teams focused on solving research-intense business problems by leveraging Machine Learning, Econometrics, Statistics, and Data Science. Postdoctoral Scientists will work at the intersection of ML and systems to solve practical data driven optimization problems at Amazon scale. Postdocs will raise the scientific bar across Amazon by diving deep into exploratory areas of research to enhance the customer experience and improve efficiencies. Please note: This posting is one of several Amazon Postdoctoral Scientist postings. Please only apply to a maximum of 2 Amazon Postdoctoral Scientist postings that are relevant to your technical field and subject matter expertise. Key job responsibilities * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise.