The absence of advanced analytical tools has been a major obstacle in the world of ball badminton, a traditional sport. The lack of real-time data and valuable insights has long impeded players, coaches, and spectators. This paper introduces an innovative system aimed at transforming the ball badminton experience, enhancing player performance, engaging spectators, and providing valuable tools for coaches and fans through real-time insights and dynamic performance visualizations. This approach involves developing a system, preferably an application, to track the movement of the player and measure position, acceleration, and speed using computer vision and predicting tactics to assist during training, tracking the short trajectory of the ball to assist during practices for improvement, displaying live statistics such as player stats (points won, errors, aces), match progress (scoreboard), and historical data (head-to-head records) to provide context and analysis during the match, besides assisting the umpire in making decisions. Benefits to the viewers are the display of real-time statistics, fan engagement through quizzes on the sport, and result prediction. Coaches can use it to make strategic decisions, players to review their performance, and fans to gain deeper insights into the match. Additionally, it employs a markerless motion capture system, which relies on deep learning for human position estimation and computer vision techniques. To transform pixel data into court coordinates and enable the measurement of parameters like distance covered, court positioning, and average player speeds for ball badminton players, the methodology relies on the inverse perspective mapping method. One of the algorithms applied in this context is the Lucas-Kanade optical flow algorithm, which aids in player tracking and position estimation, contributing to the overall effectiveness of the technology. The technology will track player movements and tactical insights with great precision. The accuracy of the system’s visuals and data is intended to considerably help to a better comprehension of the game. We aim to improve player performance, captivate spectators, and provide vital tools for players, coaches, and fans by providing real-time insights and dynamic performance visualizations.

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Real-Time Insights and Dynamic Performance Visualizations for Unprecedented Impact in Ball Badminton

  • M. Rohith Gowda,
  • Aditi N. Pai,
  • C. N. Vishwanatha,
  • M. P. Akash,
  • G. H. Ranganath,
  • H. N. Bhaskar

摘要

The absence of advanced analytical tools has been a major obstacle in the world of ball badminton, a traditional sport. The lack of real-time data and valuable insights has long impeded players, coaches, and spectators. This paper introduces an innovative system aimed at transforming the ball badminton experience, enhancing player performance, engaging spectators, and providing valuable tools for coaches and fans through real-time insights and dynamic performance visualizations. This approach involves developing a system, preferably an application, to track the movement of the player and measure position, acceleration, and speed using computer vision and predicting tactics to assist during training, tracking the short trajectory of the ball to assist during practices for improvement, displaying live statistics such as player stats (points won, errors, aces), match progress (scoreboard), and historical data (head-to-head records) to provide context and analysis during the match, besides assisting the umpire in making decisions. Benefits to the viewers are the display of real-time statistics, fan engagement through quizzes on the sport, and result prediction. Coaches can use it to make strategic decisions, players to review their performance, and fans to gain deeper insights into the match. Additionally, it employs a markerless motion capture system, which relies on deep learning for human position estimation and computer vision techniques. To transform pixel data into court coordinates and enable the measurement of parameters like distance covered, court positioning, and average player speeds for ball badminton players, the methodology relies on the inverse perspective mapping method. One of the algorithms applied in this context is the Lucas-Kanade optical flow algorithm, which aids in player tracking and position estimation, contributing to the overall effectiveness of the technology. The technology will track player movements and tactical insights with great precision. The accuracy of the system’s visuals and data is intended to considerably help to a better comprehension of the game. We aim to improve player performance, captivate spectators, and provide vital tools for players, coaches, and fans by providing real-time insights and dynamic performance visualizations.