Towards Machine Learning Framework for Badminton Game Analysis Using: TrackNet, FRCNN, and MoveNet-RNN
Badminton, a widely popular sport, holds significant potential for players, coaches, and researchers to gain valuable insights through game analysis. However, existing analysis methods often come with high costs and resource requirements. Machine learning techniques offer a promising avenue for automating and enhancing badminton game analysis while maintaining affordability and accessibility. This study explores the application of three machine learning models, namely modified TrackNet, FRCNN, and MoveNet-RNN, for analyzing badminton games. The dataset utilized for training and evaluation is expanded to facilitate accurate shuttlecock tracking and comprehensive shot type classification. Performance evaluation is conducted based on accuracy and computational efficiency, encompassing shuttlecock tracking, player monitoring, and shot type classification using a custom badminton game dataset. The findings demonstrate the superior shuttlecock tracking capabilities of the modified TrackNet model, while the MoveNet-RNN model exhibits exceptional performance in shot type classification. Leveraging recurrent neural networks (RNN) in MoveNet-RNN enhances the handling of sequence-related data, resulting in improved shot classification accuracy. This study introduces an affordable analysis framework as an initial step toward the development of a recommendation system aimed at enhancing the gameplay of amateur players.
Committee: Simanta Mitra (co-major professor) and Gurpur Prabhu (co-major professor)
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