Computer vision, the ability of computers to understand and interpret visual information, is revolutionizing the world of sports in many ways. At Strong, we've helped customers leverage historical broadcast data as well as building and deploying custom cameras in an arena or stadium to leverage computer vision to revolutionize sports. Here, we discuss some of the applications that are "changing the game" (pun intended).
Computer Vision for Sports
Computer vision is a branch of machine learning that deals with algorithms for processing, analyzing, and understanding images and video. It is used in many fields including robotics, agriculture, automotives, medicine, sports and more. Recent advances in computer vision on tasks like object recognition, pose estimation, image classification, and object tracking have been incremental to unlocking a broad array of applications across many domains including sports.
Computer vision has become an integral part of sports today as it plays a large role in many aspects of the game: from performance analysis to improving training methods and enhancing the viewer experience.
One of the most significant ways that computer vision is changing sports is through performance analysis. By using computer vision approaches including detection, tracking, pose recognition, and projection to 3d real-world coordinates, coaches and analysts can track and analyze player movements and game data in real-time. This can provide information on players' speed, acceleration, distance covered, as well as the trajectory of passes and shots.
Using this information enables players and coaches, both professional and amateur, to identify areas of weakness and make more informed decisions about training and strategy. For example, a coach can use computer vision to monitor a pitcher's arm angle and identify areas for improvement before the next match begins (or even during the match!).
Similarly, computer vision can be used to analyze interactions between players within a match. Coaches can monitor passes between players as well as the defensive and offensive positioning. This can be used to identify patterns and strategies in a team's play and to make adjustments to tactics and formations.
Improving the Fan Experience
Another way that computer vision is changing sports is by enhancing the fan experience. Sports organizations across the world are starting to use computer vision technology to create more engaging and interactive experiences for fans. By tracking player movements and positions on the field or court, broadcasters can create fun and interesting visualizations that were not previously possible. For example, by providing these real time data to broadcasters, they can provide viewers with useful information about what's happening on the field or court. This can include things like player stats, instant replays, coaching strategies, and even injury reports.
Improving Player Safety
Computer vision is also being used to improve the safety of athletes by providing real-time monitoring of player movements, helping to prevent injuries. Using computer vision, we can track a player's biomechanics including joint angles and muscular activations during different movements like running, jumping, or throwing. This information can be used to detect potential biomechanical issues, helping coaches and trainers to intervene before an injury occurs. Additionally, computer vision technology can be used to monitor the condition of the playing field or track surface, ensuring that it is safe for players to perform.
Computer vision is rapidly changing sports both amateur and professional, providing new opportunities to improve performance analysis and coaching, enhance the fan experience, and safety of the players. As the technology continues to evolve and become more sophisticated, we can expect to see even more ways in which computer vision technology will revolutionize the world of sports.
Contact Strong to find out how we leverage computer vision to build state of the art products across several sports, from custom on-premise cameras to analyzing historical broadcast data.