Speaker:Amna Mohamed

Towards Machine Learning Framework for Badminton Game Analysis Using TrackNet and YOLO Models

Badminton is a popular sport played worldwide, and analyzing the game can provide valuable insights for players, coaches, and researchers. The current methods available for analyzing badminton games can be costly and resource-intensive. Machine learning methods have the potential to automate and enhance the analysis of badminton games. The low-cost implementation of these methods makes them accessible to a wider audience, providing an affordable option for analysis and improvement. In this report, we explore the use of popular object-tracking machine learning methods - TrackNet and YOLO - to analyze badminton games. TrackNet is a deep learning-based algorithm that can track the trajectory of a shuttlecock, while YOLO is an object detection algorithm that can identify players and their positions on the court. Using a custom dataset of badminton games, we evaluate the performance of these methods in terms of accuracy and computational efficiency in tracking players, tracking the shuttlecock, and identifying shot types. Our results show that TrackNet can accurately track the trajectory of the shuttlecock, while the YOLO model can identify players and detect shot types with high precision. This research introduces a tracking system that represents the initial stage in developing a recommendation system aimed at amateur players to enhance their gameplay.

Committee: Simanta Mitra (co-major professor) and Gurpur Prabhu (co-major professor)