Golf is widely recognized as one of the most popular sports globally. However, one drawback of playing golf is the relatively high cost of equipment and coaching. While numerous training programs are available to assist players in their practice, there is currently no swing analysis program developed by Thai professionals. In this project, advanced deep learning models were employed: SwingNet, capable of predicting the sequence of eight golf swing events in videos and determining the confidence level of each swing, and MoveN et, designed to identify joint positions on the body and represent them as skeletons. These models were integrated into a customized template-matching algorithm that utilized angle-based measurements to analyze the sequence of golf swings. This analysis assessed the similarity score, represented as a percentage, between two individuals for each golf swing event. Furthermore, various techniques were implemented to enhance the efficiency of SwingN et. Through performance evaluation, it was observed that the efficiency of SwingN et surpassed by one percent compared to the pre-trained model.
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Deep Learning-Based Golf Swing Sequence Analysis
Semantic Scholar · Computer Science · 2023
Abstract
Golf is widely recognized as one of the most popular sports globally. However, one drawback of playing golf is the relatively high cost of equipment and coaching. While numerous training programs are available to assist players in their practice, there is currently no swing analysis program developed by Thai professionals. In this project, advanced deep learning models were employed: SwingNet, capable of predicting the sequence of eight golf swing events in videos and determining the confidence level of each swing, and MoveN et, designed to identify joint positions on the body and represent them as skeletons. These models were integrated into a customized template-matching algorithm that utilized angle-based measurements to analyze the sequence of golf swings. This analysis assessed the similarity score, represented as a percentage, between two individuals for each golf swing event. Furthermore, various techniques were implemented to enhance the efficiency of SwingN et. Through performance evaluation, it was observed that the efficiency of SwingN et surpassed by one percent compared to the pre-trained model.