Computer Vision based advancements in sports analytics

Sports Analytics

As technology continues to evolve, so does the potential for a wide range of applications in sports. Coaches have been able to leverage data science to analyze player performance, strategize game plans, scout opponents, and identify weaknesses in their own teams. Most importantly, computer vision is becoming highly significant in all sports, optimizing training sessions, and ensuring referees make the proper calls.

By employing AI and computer vision, companies can automate a wide range of tasks related to sports, such as optimizing player performance, analyzing game footage, and tracking analytics. In fact, the use of AI and machine learning has enabled sports teams to be better equipped with data-driven insights. This progress not only opens up new ways to entertain spectators but also revolutionizes the fan experience.

Without a doubt, technological advancements like computer vision are profoundly changing how sports are played, with applications used in training and analysis to improve athletes’ performance.

In this article, we will go over the advantages of applying computer vision in sports and present some interesting application cases.

How computer vision in sports works

Computer vision is a branch of artificial intelligence and machine learning that creates methods for teaching computers to decipher and grasp the contents of images. With the aid of this technology, machines can categorize and identify items in images as well as determine where those objects are located. By using deep learning models to precisely detect and classify items in the constantly evolving physical world, it seeks to reproduce some of the intricacies in the human visual system and visual perception.

Sports video analytics systems are becoming a very profitable business for many sports stadiums and clubs. Even though developing such systems necessitates planning the synchronized operation of numerous cameras and sophisticated processing resources, the work is typically well worth it in the long term. This equation includes computer vision. It is a perfect tool for sports analytics to obtain accurate information when taking a video of the sports field is inadequate. The position of each player on the opposing team at any one time can be determined by mathematical processing of the live stream.

It allows organizations to optimize the data by tracking audience engagement and reaction in stadiums, teams, and leagues. The information gathered can be applied to improving customer satisfaction. Also, it expands both the audience and the revenue.

Advantages of Computer vision in sports

Every facet of the sport, including the spectator experience, training, and decision-making by referees, is impacted by computer vision. Let’s examine some use case scenarios from the realm of sports.

Increasing viewer satisfaction

The use of computer vision technology has enabled sports broadcasters to follow players’ movements in real-time. It has the potential to provide viewers with a more interesting and engaging experience. To begin with, cameras now automatically identify where the action is rather than just offering a panoramic view of the entire pitch.

Secondly, teams can keep an eye on the emotions of the crowds while the games are being played, which will allow them to compile data on fan involvement and determine whether they need to enhance the fan experience.

Player tracking

Tracking players is a primary goal when using computer vision in sports. Player tracking using computer vision can provide the data coaches need to analyze a game. It allows them to identify areas of weakness and helps the team work on improving their performance. Teams can build an accurate picture of a match by understanding these aspects of the game.

Applications for computer vision can employ segmentation methods to detect areas that relate to players. The data collected can subsequently be enhanced via data mining and machine learning.

Applications provide semantic information to provide context for player actions when they recognize important features in video or image frames. Applications can build and preserve metadata for additional statistical analysis by tagging these semantic events. These insights are critical for coaches to be able to transfer players to their optimal location on the pitch. This data can then be used to spot trends and create predictive models that can be used to offer detailed explanations of how the game is played.

Making training sessions better

Players must be able to learn from their own errors as well as those of their opponents if they hope to advance. Because of this, insight-based analysis and autonomous sports analysis are crucial to the training process. Without a coach, no one can identify faults coaches frequently overlook crucial information the first time around.

They can assess player performance with the aid of computer vision. Object recognition software, meanwhile, may track an athlete and point out any flaws in their technique. Athletes and teams try to permanently break harmful habits based on this.

Ball tracking

Monitoring the movement of the ball is crucial for gathering data from ball-based sports, notably racket or bat-and-ball games like tennis, cricket, badminton, and others. The movement of the ball can be captured in three dimensions frame by frame using computer vision models.

Several camera angles and adaptable motion capture are crucial in this situation. The major goal of this is to accurately state whether the ball was in or out of bounds during the play. The underlying algorithms may construct forecasts of ball trajectories depending on numerous conditions at their deepest, most complex layers.

Review of referee decisions

Consider the last time you witnessed players encircling the referee in protest at a supposedly “poor” call. If only there was a means to verify whether the referee made the correct decision. We can now examine things like offsides, outs, goals, and picture finishes in races using 3D simulations and video analysis thanks to computer vision. Forget contentious decisions; technology is here to guarantee that every choice is the right one.

Safeguarding athletes

Sport is a terrific method to stay healthy, as we all know, but there are risks involved. However, with the use of computer vision, we can avert mishaps or even save lives. Large data sets, such as those pertaining to player position, game type, equipment utilized, playing surface, environmental conditions, and player injury, can be analyzed by algorithms, which can subsequently aid to enhance injury treatment and rehabilitation, ultimately enabling injury prevention.

Limitations and difficulties

Sports computer vision heavily relies on camera systems to capture and subsequently process sports footage. Typically, several cameras are positioned around the scene of the event, such as the stands during a sporting event or the sidelines of a practice field. Even within a single match, the angle, location, hardware, and other shooting setups vary greatly from sport to sport. Computer vision systems must also be tailored to matches and methods of footage acquisition, which presents a challenge. Additional difficulties include:

  • Advanced video equipment is lacking in many sports organizations and performance analysis departments.
  • Broadcast cameras regularly shift their pan, tilt, and zoom which creates extra hurdles for computer vision video processing systems to respond to the constantly changing data they receive.

This difficulty is because computer vision systems rely heavily on patterns and recognition algorithms. It can be complicated when differentiating between similarly appearing items such as players and objects, objects and the background, players wearing the same clothing, and other circumstances. To a certain extent, computer vision has addressed these flaws.

Computers can now distinguish between the ground, players, and other foreground objects because of image processing, for instance. In contrast, color-based segmentation algorithms enable the detection of the ball, tracking of moving players, and localization of the pitch zone by the green.

Wrapping Up

Artificial intelligence is making its way into a wide range of sports, including football, baseball, and even golf. In the long term, it simplifies coaching, training, refereeing, analyzing, highlighting, and gratifying audiences easier to understand and more effectively. In this post, we discussed some of the most prevalent applications of computer vision in sports as well as some of the challenges associated with its use.

The most common tasks related to computer vision in sports comprise tracking players and balls, estimating poses to prevent injuries, segmenting images to separate players from backgrounds, and more. Yet, when it comes to deploying an AI-based system for sporting events, you have the option of choosing known yet expensive solutions or buying a custom local system that is both affordable and effective.

Our team at Vadzo is skilled at creating and integrating high-performance camera systems that can provide videos and images that can be fed to complex CV-based software. If you are looking for high-performance cameras such as HDR camera, Low-Light camera, High frame rate cameras, feel free to Contact Us

 

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