Do you exercise to stay fit, or are you perhaps a lover of cricket or football? Others love watching games with friends.
Some people participate in sports to be healthy and attentive. Sports are unquestionably a significant aspect of our lives, regardless of our interests or way of living.
Sport, like every other important aspect of our daily lives and the global economy, is unavoidably impacted by technological improvements.
Today, in 2022, sensor-equipped F1 vehicles and real-time football analytics are not futuristic tech fancies.
In reality, the advancements go much further: the most advanced businesses have already used computer vision and artificial intelligence in sports to meet a variety of issues.
There is little question that artificial intelligence and machine learning will continue to advance this discipline given the significant influence that technology has had on sports.
This article will concentrate on the use of computer vision in sports, including practical applications, advantages, and much more.
We will start with the introduction of computer vision.
So, what is computer vision?
The field of artificial intelligence and machine learning known as “computer vision” (CV) aims to develop techniques for teaching computers how to comprehend and comprehend the contents of pictures.
In order to recognize and classify objects in a dynamic and changing physical environment, computer vision uses deep learning models to simulate some of the complexity of human vision systems and visual perception.
The computer makes an effort to mimic how a person sees the visual environment.
However, unlike people, computers have the capacity to store enormous amounts of data and process it swiftly, giving us the flexibility to delegate many chores to the most cutting-edge technologies.
Today, advances in smartphone technology, social media, and their widespread usage by billions of people – more than 3 billion photographs are posted online every day – are creating even more visual data than ever before.
Together with increased access to large computing power and advances in deep learning and neural network algorithms (e.g., the invention of convolutional neural networks), the availability of such massive amounts of images has provided computers with invaluable opportunities to learn the patterns and characteristics of these images and improve the accuracy rates for object detection and classification.
As a result, computer vision systems have achieved accuracy rates of 99 % in a number of their applications, surpassing the accuracy of human vision in specific detection, categorization, and response tasks.
Computer vision in Sports: Real-World Examples
1. Player Tracking
Player tracking is one of the main goals when using computer vision in sports. In order to do this, it is necessary to identify each player’s location at any given time.
Coaches can rapidly analyze how each player moves on the field and the structure of their team thanks to player tracking, which is a crucial component in helping teams perform better.
The most cutting-edge computer vision applications in sports nowadays employ automatic segmentation algorithms to pinpoint areas that probably belong to athletes.
By utilizing machine learning and data mining methods on the unprocessed player tracking data, the output of a computer vision system can be improved.
Semantic information can be created once crucial components in an image or video frame have been identified to put the activities the participants are taking in perspective (i.e. ball possession, pass, run, defend, and so on).
These methods can be used to classify semantic occurrences, such as a “one-two pass” in football, and to do extensive statistical analysis of the performance of individual players and teams.
In order to allow coaches to compare ideal player placement with actual player positioning during a specific play, suggestions can also be made on the best places for players on the field.
The numerous options brought forth by this player tracking technology have the ability to completely change how athletes prepare and are scouted.
2. Injury prevention
To address the increased need for mental rewiring and well-being in the face of social distance, many people are resorting to online courses.
In order to learn how to exercise safely and prevent injuries, it is important to try a few classes taught by an experienced instructor, whether in a private or group setting.
For instance, both pilates and yoga are simple enough to do at home. However, especially for a beginner, it is important to try a few classes. Computer vision, in particular posture estimation, comes into play in this situation.
Posture estimation is a computer vision job that aims to anticipate and monitor a person’s or object’s location, and 3D pose estimation-based apps are now available to help human fitness trainers.
These technologies evaluate every action of the user and offer them thorough real-time feedback using a wealth of motion tracking data.
Receiving real-time feedback and avoiding workout injuries are two benefits of working together with a virtual coach.
3. Ball tracking
For information extraction from ball-based sports, particularly racket or bat-and-ball sports like tennis, cricket, badminton, and others, tracking ball movement is crucial.
Computer vision models can indicate the precise location of a ball’s impact with the ground, record the movement of the ball in three dimensions, and even forecast the trajectory of the ball to assess if it would have struck the wicket.
In other terms, ball tracking systems driven by computer vision help with:
- Detection of balls
- Tracing the trajectory
- Game outcome forecast
This type of ball-tracking is more challenging in games like basketball, volleyball, and soccer because the ball can be concealed behind the players. Alternately, player exchanges with the ball might happen quickly and without warning.
4. Referee Decision Improvement
There have been innumerable examples of blatant cheating and incorrect referee decisions throughout the history of sports. Through the years, technology has made its way into sports, helping to reduce the number of mistakes that referees make.
With the introduction of technologies like Video Assistant Referee (VAR), Goal-Line Technology (GLT), Hawk-eye, Decision Review System (DRS), and Hawk-eye in tennis and cricket, umpire or referee decisions can now be reviewed and, if incorrect, overturned.
Future sports officials will make even fewer mistakes because of the growing usage of AI and computer vision.
5. Pose estimation in mobile application
Utilizing cutting-edge technologies will motivate people to utilize your program frequently.
How frequently have you come across applications that use videos to demonstrate how to perform workouts properly?
Most likely lately fairly regularly. And consider developing a computer vision model that automatically sets the proper position, keeps track of the approaches made, and offers tips on how to enhance your workout. a fantastic stand-in for a genuine coach.
With this kind of application, training is always accessible; all you need is a camera on hand. Develop your area of expertise by adding your own particular postures and techniques to stand out in your market without having to pay more for human teachers.
This technology is very helpful for honing your specialty, which can be certain postures or motions. You don’t need to pay for extra professional trainers to teach your programs.
6. Journalism and sports content
You can produce intriguing content by combining artificial intelligence and computer vision technologies.
The camera will automatically move closer to the most intriguing time when the model analyzes events, such as a goal.
Imagine if you just need to set up a few cameras that can intelligently and automatically focus on the most crucial parts of the game rather than having to pay a large number of reporters and wait for post-production to publish sporting events.
7. Fan mood
The range of computer vision applications is simply astounding. The enjoyment of a person viewing something could previously be measured by tests that involved the attachment of special wires to detect impulses.
We no longer need to confine every viewer to a laboratory thanks to computer vision technologies. Get a thorough examination of the satisfaction of moviegoers.
Many different emotions, such as happiness, boredom, excitement, disappointment, etc., can be distinguished by computer vision models.
Sports computer vision mainly relies on camera systems to capture and then analyze sports footage. Typically, a number of cameras are positioned around the scene of the action, such as the stands during a sporting event or the sides of a practice field.
Even within a single match, the angle, location, hardware, and other shooting settings vary greatly from sport to sport.
Computer vision systems must also be adapted to certain matches and methods of film capture, which presents a problem. Additional difficulties include:
- Many sports organizations and performance analysis divisions lack advanced video equipment.
- The frequent pan, tilt, and zoom changes made by broadcast cameras make it more difficult for computer vision video processing systems to adapt to the constantly changing data they receive.
- It may be difficult for computer vision video processing systems to distinguish between items in the backdrop, players, and objects, players wearing the same attire, and other situations.
To a certain extent, computer vision has solved these flaws. For instance, image processing has allowed computers to discern between the ground, players, and other foreground items.
Otherwise, color-based segmentation algorithms make it possible to recognize the ball, monitor moving players, and locate the pitch zone by the color of the grass, which is green.
To summarize, computer vision is the most popular technical field, and its popularity is only growing. This is a fresh perspective on data processing and how it is seen; we have finally trained computers to see.
The most common computer vision tasks in sports are player and ball tracking, posture estimate for injury prevention, segmentation for distinguishing backdrop from players, and others.
Every day, we generate a vast quantity of data that we may utilize to effectively train models, which will then function as hopeful assistance in addressing business difficulties.