Do you train to be fit, or are you a cricket or any other sports fan? Some people play sports to maintain health and awareness; others enjoy watching games with friends. Sports are integral to our reality regardless of our lifestyle and preferences. Like any other significant area of our daily lives and the world economy, sports is inevitably subject to technological advances.
Today, in 2023, real-time sports analysis is no longer a distant technological fantasy. Progress goes far beyond that: the most developed companies have already employed artificial intelligence and computer vision in sports to address various challenges.
Given technology’s significant impact on sports, there is no doubt that artificial intelligence and machine learning will continue to push this field forward.
In this article, we listed the benefits of computer vision in sports and highlighted some fascinating use cases.
Main benefits of computer vision in sports
The use of AI in sports is fastly growing, and computer vision plays a role in every sports aspect, from visual experience to coaching to how referees make decisions. Let’s look at four typical use cases in the world of sports.
1. Enhancing the spectator experience
What would sports be without fans cheering their teams on at every game? Now, thanks to computer vision, broadcasters can improve the fan experience. Cameras now know where to focus, automatically finding the action instead of simply offering a panoramic view of the entire field.
Also, clubs can monitor fans during matches and analyze their emotions, which they can use to create statistics on fan engagement, helping them understand whether they need to improve the fan experience.
2. Improve training sessions
To improve, players must learn from their own and their opponent’s mistakes. For this reason, automated sports analysis and insight-based analysis are essential to the coaching process.
Can you spot mistakes on your own if you train without a coach? Probably not, which is why everyone, even professional players, must be monitored!
In turn, a coach could easily miss essential training details, which is why computer vision comes into the picture, which can help coaches analyze player performance.
Meanwhile, object recognition software can follow an athlete and highlight weaknesses in their technique. On this basis, athletes and teams work to eliminate bad habits once and for all.
3. Checking the referee’s decisions
Remember the last time you saw players surround the referee, angry about an alleged “bad” decision? If only there were a way to check whether the referee made the right call. Thanks to computer vision, we can do just that by using 3D simulations and video inspections to check off sides, outs, goals, and photo finishes in competitions. Forget about controversial calls; the technology is here to ensure that every decision is correct!
4. Keeping athlete’s safe
As we all know, sports are a great way to stay healthy, but not without risk. But thanks to computer vision, we can help prevent accidents or even save lives (more on this later in the article on a particular example).
Algorithms can analyze large data sets, including information about the player’s location, type of play, equipment used, playing surface, environmental factors, and player injuries, then help improve injury treatment and rehabilitation, ultimately enabling injury prevention.
In addition, by understanding the factors that cause injuries, officials can introduce changes to protect athletes better. Interestingly, the data influenced the NFL’s starting rules, resulting in a 38 percent decrease in concussions on restarts.
Examples of the use of computer vision in sports
We have seen how computer vision can improve the sports experience. Now, it’s time to move on to some new use cases.
Here are four ways you can use computer vision in sports today:
1. Mapping athletes’ positions
Events such as marathons and bicycle races can involve thousands of participants. So how can you find your favorite athlete among the masses? Computer vision can analyze footage and identify people based on attributes such as jersey numbers. This can also help organizers automatically update spectators on results.
2. Ball tracking
If you are a tennis or cricket fan, we are sure you will be familiar with Hawk-Eye. It is a technology that uses cameras to track the ball’s trajectory and then uses artificial intelligence to predict where the ball will land with an accuracy of a few millimeters.
Ball tracking technology is everywhere, helping tennis umpires know which side of the line the ball fell on. It is now one of the most popular sports AI applications, offering two prominent use cases:
- Showing rotation and direction: helping athletes understand how their position affects the strike of the ball;
- Supporting referee calls: wondering if the ball was in or out?
3. Real-time player monitoring
Sports analytics might help you improve your performance in the next event, but real-time monitoring can improve it immediately. For example, Sentio is a soccer player tracking system that uses computer vision and machine learning algorithms. And it can monitor the performance of players and teams in real-time by linking each cell in the footage to a unique ground point, represented by a fixed image patch.
4. Analysis of opponents
A well-thought-out strategy is one of the most critical factors in winning. But how can coaches come up with one? They usually rely on footage of their opponents; software like GAMEFACE can help.
GAMEFACE’s analysis process consists of three simple steps:
- Upload the match footage
- GAMEFACE analyzes it using artificial intelligence
- Customized reports highlight critical information
The software enables coaches to analyze opponents, then formulate an effective match strategy based on complex data.
Conclusion
Artificial intelligence is entering various sports, from cricket to soccer and even golf. In this article, we have touched on some of the most common use cases of computer vision in sports and illustrated examples of existing applications, which are likely to become even more technological as the years go by.
