Imagine a world where referees never miss a critical decision, from offside calls to penalty spots, thanks to technology. Enter computer vision, redefining how sports officials interact with the game. Read on to find out how AI is enabling better refereeing and decision making with advanced computer vision.

For years, technology has been making its way into sports, from performance tracking to fan engagement. Prior to AI-driven technology such as VAR, football refereeing decisions were less reliable. A study of over 800 matches from more than 20 countries discovered that the total accuracy in refereeing decisions increased from 93% to nearly 99% according to BMC Sports Science Medicine and Rehabilitation institute, in the four categories where VAR intervenes, demonstrating the significant improvement in decision-making accuracy with the integration of AI technologies.

However, one of the most transformative applications has been in officiating, where computer vision is allowing referees to make faster, more accurate decisions. According to Sports Business Journal, the first ever computer vision tracking system used was Prozone during a game featuring then Premier League club Derby County in 1997.

By processing video feeds in real time, computer vision can analyse plays, detect fouls, and assist referees in making the most informed calls, improving fairness and reducing errors. This is still in development but it is the case for the Hawkey Eye system in Tennis or VAR in Football.

Accurate and timely decision-making is critical in high-risk situations such as penalty decisions or red card calls. Computer vision ensures that referees receive unbiased, real-time data, making rulings more dependable than ever before.

IBM Watson Data Center at Wimbledon, photo by Dinis Guarda

What is Computer Vision in Sports?

Computer vision is a type of artificial intelligence that allows computers to interpret and comprehend the visual world. In sports, computer vision systems analyse video data to distinguish objects (such as players and the ball), track movement, and provide referees with real-time information to help them make better decisions.

 

Lets explore how it works:

Image Recognition: Identifying objects like the ball crossing the goal line or players in offside positions. These are mostly used in Tennis for example to get to know if the ball fully crosses the lines or if it is within the marked zones.

Motion Tracking: Monitoring player movements, helping detect fouls or offsides. It is often at play, especially more recently with the adaptation of sensors inside balls in football for example to get a deeper knowledge of the trajectory of the ball.

Contextual Decision-Making: AI systems assist referees by offering real-time insights on penalty box activity or goal line technology. Its also the case for coaching and managerial teams, that use this resource to get to understand what tactics fit best according to the game at play: like in American Football or Basketball (where for example: getting to know the best suited athletes according to the situation of the game is crucial to try to get a crucial lead over the opponent)

 

The technological components of the system:

Camera Systems: High-speed cameras capture multiple angles of the game. Like during football matches where camera systems are used to check offsides or in the case of goal line technology: to help referees in understanding better if the ball crosses the goal line or not.

AI/ML Models: These models recognize patterns and detect events like fouls or goals. It can be the case in sports like Formula 1 or Fencing: where fouls are more easily detected through the use of AI, here either it being car damage or mistakes in touches in Fencing.

Data Processing & Cloud Computing: Instantaneous data analysis ensures that decisions are made in real time. An example of this can be Baseball where strategies can be defined according to data about the events of the game: target and speed of the wind, precipitation,...

Key Applications of Computer Vision in Refereeing

Tracking and Helping with offside decisions (Football): VAR (Video Assistant Referee) uses computer vision to track player positions and determine offside calls. In the 2018 FIFA World Cup, VAR corrected offside decisions that were missed by on-field referees, proving the system's worth.
 

Goal-Line Technology in Football: Hawk-Eye's goal-line technology in football ensures that every goal is precisely validated; it famously reversed the controversial no-goal ruling during the 2010 FIFA World Cup, when Frank Lampard's shot against Germany was incorrectly ruled out.

The case of Tennis (Hawk-Eye): Hawk-Eye is also utilised in tennis to ensure impartiality during line calls. During important matches such as Roger Federer vs. Novak Djokovic, Hawk-Eye has determined whether a ball fell in or out, providing players and referees with a clear, unbiased response.

Some examples implied in Rugby and American Football: In rugby and American football, computer vision is used to track player locations during scrums and tackles, assuring fairness. In the NFL, for example, the approach has helped to make more accurate calls under high-pressure situations.

Benefits of Computer Vision in Refereeing

Although some have argued that this form of technology violates some of sports' conventional principles, it is clear that it has been useful in fixing mistakes made during games. Some of the arguments in favour of this approach include the following:

Accuracy and Precision: Computer vision eliminates human errors in complex decision-making, such as offside or penalty calls. By processing data instantaneously, it ensures decisions are made with greater accuracy than traditional methods.

Fairness and Transparency: Decisions based on concrete, unbiased evidence reduce controversies and increase transparency. Fans and players can trust that calls are made objectively, ensuring the integrity of the game.

Speed and Efficiency: Instant data processing allows referees to make quick decisions. For instance, tennis players in Grand Slam events get immediate results from Hawk-Eye, reducing delays during matches.

Challenges and Limitations

Despite its effectiveness, computer vision systems can be hindered by technical issues like camera malfunctions or poor lighting, which may affect the quality of the footage and decision-making accuracy.

Another point proving these kinds of barriers is that smaller leagues or less-funded sports may struggle to implement such technology due to the high costs of camera systems and AI models.

Another argument is that despite the contributions of these technologies, they are still dependent on human factors, while computer vision is a powerful tool, referees remain in control, making the final decisions. This balance sometimes leads to disagreements between technology-driven insights and human judgment.

Future Trends in Computer Vision for Refereeing

The future of computer vision in sports officiating is linked to advances in artificial intelligence and machine learning. Artificial intelligence-powered systems improve decision-making accuracy and speed by analysing complex data in real time. For example, the Premier League is planning to implement an AI-powered camera system to accelerate VAR decisions, dramatically cutting referral times. This integration enables more precise offside calls and faster decision-making, improving the overall flow of the game.

Looking ahead, there is the possibility of totally computerised officiating in certain elements of the game. Semi-automated offside technology, for example, is currently in use, with future advances potentially leading to more autonomous systems. These developments aim to eliminate human error and improve officiating uniformity at all levels of play.

The use of computer vision technologies is not confined to traditional sports. Emerging applications include basketball, cricket, and esports, where AI-powered solutions are being created to help with decision-making and improve the spectator experience. For example, AI systems are being used to analyse player movements and game strategies, offering more information on performance and tactics. These advancements are increasing the scope of computer vision in sports, making it a necessary component of the officiating scene across multiple disciplines.

Case Study: A Major Sports Tournament Implementing Computer Vision

The case of the FIFA World Cup (VAR & Hawk-Eye): The introduction of VAR and Hawk-Eye at the 2018 FIFA World Cup marked a significant step in improving officiating. According to reports, VAR corrected crucial decisions during the tournament, leading to a reduction in on-field controversies and an increase in the accuracy of match outcomes.

Radical reforms in Wimbledon (Tennis) & Hawk-Eye: In tennis, Hawk-Eye has been a game-changer. At Wimbledon, the system has been used since 2007, providing real-time decision assistance for line calls. Players like Novak Djokovic and Roger Federer have praised the system for its accuracy and fairness, marking it as one of the most reliable technologies in sports.

​The integration of computer vision into sports officiating is revolutionising decision-making by enhancing accuracy, speed, and fairness. By processing video feeds in real time, these systems assist referees in making informed calls, reducing human error, and increasing transparency. As AI and machine learning technologies evolve, their application in sports officiating is expected to expand, leading to more precise and consistent decisions across various sports. This technological advancement not only improves the quality of officiating but also enhances the overall integrity of the game. As highlighted in The Sports ABC of Technology: Revolutionising Sports with AI and Smart Innovations by Dinis Guarda, the increasing reliance on computer vision is poised to revolutionise the sporting world for years to come.