The Top 17 Applications of Computer Vision in Sports

17 Popular Applications of Computer Vision in Sports

From AI reviews of officiating to ball tracking to injury detection, computer vision models provide deeper insight into practice and matches than humans could ever develop on their own.

Let’s examine the top 17 computer vision applications in sports.

1. AI-Based Official Reviews

Sports computer vision now identifies rule infractions in athletic events. For instance, the UK’s Premier League employs video assistant referee (VAR) to assist referees when making judgment calls regarding disputed and pivotal moments during a match.

AI in sports relies on match recordings to assist referees in determining whether a player has scored a goal, committed a foul, or gone offsides. The system delivers precise results, often surpassing the capabilities of human vision.

2. AI-Supported Rehab

Computer vision also observes the recovery progress of athletes. Scientists have developed various computer vision tools, including those designed to monitor the range of motion in athletes with shoulder impairments.

These systems precisely monitor the recovery progress, helping physicians and physical therapists create more efficient rehabilitation plans. By observing athletes’ movements throughout the healing process, computer vision offers meaningful data to customize exercises for each person.

3. Athlete Identification

A key application of computer vision in athletics involves the recognition and categorization of athletes. The process uses AI software to detect and classify athletes in video recordings. Users review the gathered data to monitor players’ development over time or evaluate their sports performance against other competitors.

Staff who use computer vision in sports draw upon several techniques for athlete identification and classification in video recordings. A widely used approach harnesses facial recognition technology, which can pinpoint an athlete based on characteristics like hairstyle and facial contours. Another frequently used method involves monitoring the motion and positioning of the athlete’s body to determine their identity.

4. Augmented Scouting and Talent Analysis

Computer vision can evaluate athletes during games and practice sessions, examining their abilities, awareness, and physical traits for talent acquisition. The technology enables scouts and coaches to pinpoint emerging prospects without manually sifting through hours of video. It facilitates impartial decision-making because it can provide valuable insights into an athlete’s attributes for early recognition and proper development of potential talent.

5. Automated Film Analysis

A widely used application of computer vision in athletics involves automated video evaluation. This process employs machine learning algorithms and pattern detection to automatically retrieve information from video recordings of athletes in motion. Coaches, trainers, and players can use the extracted data to measure, analyze, and improve individual and team performance.

For instance, automated film analysis monitors an athlete’s motion patterns and pinpoints aspects that need refinement. Additionally, teams use it to study an opponent’s behaviors and develop tactical plans for a strategic advantage.

6. Automated Officiating

Computer vision in the sports industry assists with officiating decisions. For example, the Hawk-Eye technology is a computerized system utilized in cricket, tennis, soccer, badminton, rugby, and volleyball to monitor the path of the ball and determine whether it remains in play or out of bounds.

This technology employs an array of cameras and multi-angle video analysis to monitor the ball’s movement and contrast its path with a digital representation of the playing field. The system offers precision within a few millimeters, surpassing the accuracy of human vision.

The National Football League uses a platform known as Next Gen Stats to monitor the body position of each athlete on the field. This data assists referees in making more informed judgments, such as determining whether a penalty occurred.

7. Ball Tracking

Live object recognition identifies sporting gear, such as tracking a ball using AI. Object detection and ball tracking see use in basketball, football, soccer, and various bat-and-ball sports.

Artificial intelligence ball tracking utilizes machine learning techniques to monitor the motion of a ball and forecast its path. This data increases precision in shooting and passing and assists coaches in developing an in-game strategy.

8. Improved At-Home Viewing Experiences

Computer vision transforms how spectators engage with sports. It facilitates the development of augmented reality (AR) experiences that let fans view sports games from an athlete’s viewpoint or recreate the atmosphere of a stadium in the comfort of their homes.

For instance, during a football game, computer vision enriches live coverage by adding real-time statistical overlays, tracking players, and generating 3D replays, making viewers feel more immersed in the event and involved with the broadcast.

These advancements augment the fan experience and create fresh opportunities for sports organizations to generate revenue from their content.

9. In-Game Decision-Making Support

By examining match recordings, computer vision systems deliver comprehensive evaluations of team setups, tactics, and rival vulnerabilities. This technology offers coaches and athletic teams practical intelligence to fine-tune team strategies and make well-informed, real-time choices to gain a competitive advantage.

10. Injury Detection

Computer vision in the sports industry identifies and observes risk factors associated with injuries. The Los Angeles Lakers employ computer vision technology to monitor athletes’ movements during matches and training sessions. They collect AI vision performance assessment metrics to increase player efficiency and reduce the likelihood of injuries.

For instance, computer vision assesses the likelihood of knee sprains during aerobic exercises. Knee injuries make up the highest proportion of serious, sports-related trauma. Over half of these incidents involve tears to the anterior cruciate ligament (ACL). As a result, computer scientists have developed multi-perspective, automated video analysis to examine the potential for lower-limb injuries during various athletic motions.

11. Injury Prevention

Computer vision employs accurate motion tracking to minimize the chance of injury. By observing an athlete’s form and movement behaviors in real time, computer vision systems recognize abnormalities that signal a heightened injury risk. 

In activities like jogging and sprinting, for example, computer vision spots inconsistencies in stride or asymmetries that lead to injuries like stress fractures and tendinitis. Trainers and sports health specialists apply this information to modify training plans and emphasize corrective workouts, lowering the risk of harm and supporting long-term athletic well-being.

12. Outcome Forecasting

Machine learning models forecast the outcome of a sports event by monitoring athletes’ movements. By processing data gathered through computer vision, the system anticipates the victor of a competition. These insights indicate which participant has a higher statistical probability of winning.

13. Pitching and Batting Performance Analysis

AI batting and pitching computer vision plays a significant role in bat-and-ball games like baseball and cricket. In these disciplines, the objective involves striking a ball using a bat. Computer vision can monitor the ball’s flight path and anticipate the exact point of contact with the bat, assisting athletes in the development of their batting and pitching skills.

By observing the ball’s motion, a pitcher can receive insights into their throwing mechanics. These insights can then be used to make modifications that improve command.

14. Player Motion Tracking

Computer vision in sports monitors athletes’ movements in order to improve performance. By monitoring athletes’ motion, trainers can pinpoint areas for improvement, and athletes can recognize where to concentrate their practice. Computer vision person movement analysis tracks the movement of joints and limbs with greater precision.

Coaches and players can use player motion data to elevate their team’s performance by fostering an understanding of how players respond to training and identifying which aspects of their game require further development.

Computer vision artificial intelligence systems that monitor a player’s motion also detect possible injury risk indicators. For instance, if an athlete frequently lands improperly after jumping, it may suggest a heightened likelihood of sustaining a knee injury. By spotting early warning signs with AI-based movement analysis, trainers and athletes can take proactive steps for injury prevention.

15. Security and Crowd Control

Artificial intelligence vision tools oversee crowds and maintain safety at sporting events, including the Olympics, PGA Tour, FIFA World Cup, and Wimbledon. Using multiple surveillance cameras, these visual systems utilize real-time computer vision to identify and follow objects within a crowd.

AI technologies employ deep learning algorithms to identify and follow items such as bags, backpacks, and mobile devices. Dedicated applications also recognize hazardous situations, including aggressive crowds, armed individuals, and unattended belongings.

16. Sports Analysis

As computer vision technology advances, so do the manners in which teams apply it to boost athletic performance. One significant area where computer vision demonstrates major influence is large-scale visual AI sports match analysis.

Machine learning sports analytics refers to the practice of leveraging data to improve athletic output in team sports like football, basketball, soccer, hockey, and baseball. The process involves examining data related to athlete positioning, tactical approaches, and match environments. Coaches can apply these insights to boost performance by adjusting training routines, strategic planning, and player responsibilities.

The statistics and information gathered using data mining methods and deep learning models hold significant value in sports reporting. The compilation of data on performance, real-time gameplay, and post-match reviews allows sports writers to deliver a more comprehensive understanding of the event to their audience.

17. Training Analysis

A computer vision setup can monitor the number of repetitions an individual performs during a specific workout using real-time video analysis. These computer vision tools also deliver evaluations regarding the accuracy and effectiveness of each repetition.

In athletic training and sports healthcare, trainers can use this data to improve an athlete’s technique and output by providing real-time feedback derived from visual input.

AI visual recognition identifies a range of physical exercises, such as rows, squats, lunges, and push-ups. Advanced deep learning models function by analyzing body posture and tracking the motion of joints in the limbs using keypoint detection and human pose estimation techniques.

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Benefits of Computer Vision, Sports, and AI

Sports computer vision monitors and improves athletes' performance by merging advanced technology with conventional approaches to achieve better outcomes. Some of the advantages of computer vision in sports include:

• Advanced analytical tools accessible to all organizational levels
• Data-driven strategies and training minimize speculation and inform tactical decisions
• Fine-tuned performance insights that allow for targeted improvements
• Prompt identification of hazardous motions helps players avoid injuries
• Real-time insights enable coaches and athletes to modify techniques on the spot

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Challenges of Computer Vision in Sports

Although computer vision in the sports industry offers several tangible benefits, computer scientists must meet a few challenges at deployment and post-launch. Specifically, computer vision providers must maintain data accuracy and privacy while addressing the high compute demands of their cutting-edge systems.

Data Accuracy

A major obstacle to using computer vision for sports evaluation involves maintaining precision in changing environments. When implementing machine learning techniques for visual analysis, AI must correctly identify and follow human posture and motion under different lighting and weather conditions. 

Reaching the desired level of accuracy demands advanced models trained on varied data collections to faithfully interpret the subtleties of athletic movement without making mistakes. AI must overcome this technical challenge for dependable player performance tracking, injury risk reduction, and other uses.

High Compute Demands

Handling the processing requirements of analyzing high-resolution, high-frame-rate video streams in real time presents a significant challenge when developing a computer vision system. Sporting events generate massive volumes of visual data, and interpreting this information using computer vision techniques demands substantial computational resources. 

AI must strike a balance between the necessity for immediate analysis and the constraints of existing hardware to ensure the system functions reliably during live events. This difficulty underscores the importance of refining algorithms for optimal athletic performance and investigating cutting-edge computing technologies.

Integration

As computer vision technologies and athletic analytics platforms advance, emerging AI systems must merge smoothly with current infrastructure and data standards. Integration requires frequent updates and compatibility assessments, posing a challenge for sports organizations to stay aligned with technological progress while optimizing the return on their investments.

Overreliance

There is some risk that dependence on data and analytics might diminish the instinctive, personalized expertise that seasoned coaches contribute to training and tactical planning. Striking the appropriate balance between leveraging technological innovations for factual assessment and preserving the essential human element that fosters athlete growth and motivation remains essential for the well-rounded development of sports professionals.

Security and Privacy

As computer vision systems gather and analyze large volumes of data in sports, including confidential details about athletes’ health and performance, teams must maintain the privacy and security of this information. The process involves defending against unauthorized access and adhering to legal requirements and ethical principles concerning the handling of personal data. Balancing the advantages of sophisticated analytics with the protection of individual privacy demands strong security protocols and transparent guidelines.

The Top Computer Vision Technology: Edge Vision AI Platform

At AiFA Labs, we have created revolutionary new software that sports teams can use to improve the fan experience, control crowds on game day, and assess player performance: The Edge Vision AI Platform! For a free demonstration of this cutting-edge technology, fill out our online form or call AiFA Labs at (469) 864-6370.