⚽ Football Players Tracking with YOLOv5 + ByteTrack
This project leverages the power of computer vision to enhance sports experiences, specifically football. By combining YOLOv5, a popular object detection algorithm, with ByteTrack, the project aims to efficiently track football players on the field. Originally inspired by the author's previous work with basketball, this current iteration is fueled by the excitement of the FIFA World Cup 2022. The system meticulously identifies and tracks players, a task that enhances game analysis, through the use of advanced machine learning models. YOLOv5 specializes in detecting the players, while ByteTrack focuses on maintaining recognition of individual players as they move across the field. This effort is well-documented through a blog and a video guide available on various platforms, offering insights and tutorials on effectively employing these technologies for sports analytics.
🤸 3D Football Players Pose Estimation with YOLOv7
Intriguingly, the project also delves into 3D pose estimation using YOLOv7, another advanced model in the YOLO series. The inspiration for this project stemmed from VAR (Video Assistant Referee) technology used in top football matches for offside analysis, which originally piqued the author’s interest during a FIFA 2022 World Cup match. Utilizing two cameras, the project demonstrates how one can emulate pose estimation at home. Pose estimation provides an opportunity to analyze and visualize player movements in three dimensions, offering a deeper understanding of each player's on-field actions. This technique can significantly enhance coaching strategies and player performance analysis. The project showcases the ability of home-brewed systems to replicate complex technological solutions used in professional-level sports.
👕 Assigning Football Players to Teams by Uniform Color with GPT-4V
This innovative project integrates GPT-4V, an advanced vision-powered component of the renowned language model, GPT-4, to analyze football matches by team uniforms. The primary objective is to automatically assign players to their respective teams based solely on the color of their uniforms. Using GPT-4V's capabilities, the project implements sophisticated vision prompting techniques to discern team differences. This technology ensures accurate team classification, enhancing automated match analytics and game strategy evaluations. By employing such advanced AI capabilities, the project exemplifies how cutting-edge technology can simplify complex tasks traditionally requiring significant human effort.
In summary, these projects collectively portray the potential of artificial intelligence and computer vision in revolutionizing sports analytics, making the processes more efficient, detailed, and insightful. Through accurate player tracking, pose estimation, and team classification, these projects offer valuable contributions to enhancing the game experience for analysts, coaches, and fans alike.