Computer Vision Applications in Sports

Computer vision (CV) technology has improved dramatically over the past decades. According to market intelligence firm Tractica, the global computer vision and hardware market is expected to reach USD $48.6 billion by 2022 from just USD $6.6 billion in 2015. The technology is being used in various industries – from construction to healthcare and, increasingly, sports.

In this article, we’ll discuss what computer vision is and how advancements in this sector have been giving coaches and athletes major benefits, including performance and strategy analysis.

What is computer vision?

Computer vision is a subset of artificial intelligence and machine learning (AI/ML) that teaches computers to accurately identify and classify images the way humans do. Using deep learning, an ML model that utilizes several layers of neural networks, CV is able to detect and organize the relationship between objects and their environments. There are several CV models but the most popular ones are image classification, object detection, image segmentation (dividing images into parts), and facial recognition.

Big data has helped this sector to grow, as algorithms learn from more than 3 billion images shared online. As a result, today’s CV systems have reached 99 percent accuracy. This is why your smartphone is able to identify when to shoot in night mode or when to switch to food photography based on what the cameras see.

Applications of computer vision in sports

Enhanced athletic performance based on motion tracking

In 2019, tech giant IBM created a cognitive coaching system for the US Women’s National Soccer Team. The system uses video footage of players’ performances to provide timely feedback. By analyzing the athlete’s movements, coaches can pinpoint areas of improvement, and athletes can focus on training for these highlighted areas.

Computer vision has majorly improved since then to track joint and limb movements, helping coaches and athletes understand how training is impacting their performance. Additionally, CV can analyze a player’s movements to identify potential risk factors for injuries (e.g., ankle and wrist injuries through improper posture).

Player tracking for performance analysis

While detecting players and referees from camera feeds is quite easy, accurately identifying them is much more difficult. To increase player tracking precision, CV systems often use many different camera angles specifically for that purpose. Through this technique, CV can tag each player and analyze their poses, giving valuable information on live-game, real-time performance.

In addition, sports journalism benefits from precise player tracking. The data can be used to provide more in-depth strategies and post-game analysis, giving viewers educational but entertaining content. Because CV has the ability to differentiate players, sports journalists can highlight why certain athletes perform better than others.

Ball tracking

One of the most widely utilized CV technologies for ball tracking is tennis’ HawkEye. This system helps umpires accurately identify where the ball has landed, allowing them to make evidence-based decisions. Ball-tracking tools can help sports such as cricket, badminton, baseball, basketball, and football to record movement in 3D, showing where the ball precisely hits and even predicting where it will go based on player stance.

In football, FIFA collaborated with HawkEye to install goal line technology in major stadiums through a 7-camera CV system. The system uses high-speed cameras covering each goal area that can detect moving objects based on area, color, and shape. The CV tool has an error rate of 1.5 centimeters and a detection speed of 1 second, enabling referees to make real-time decisions.
Challenges and limitations of computer vision
While CV technology has come a long way, it still can’t compete with the human eye. While it can detect movements, it still has trouble analyzing some complex postures or determining which player is which during collisions and certain interactions. Sometimes, equipment (particularly new ones) confuses these cameras and sensors.

Nonetheless, tech companies are constantly tweaking their CV technologies to address these limitations. For example, image processing is being enhanced to better distinguish players from foreground objects. As professional sports become more competitive, the more CV solutions are going to be refined and expanded to provide better performance analytics.

How AI Superior can help

Does your business need computer vision solutions to boost your performance? at AI Superior, we can help you identify which processes you can automate through CV tools and systems, including image processing and segmentation, facial and optical character recognition, and object detection.