Abstract
Analysis of golf swing events is a valuable tool to aid all golfers in improving their swing. Image processing and machine learning enable an automated system to perform golf swing sequencing using images. The majority of swing sequencing systems implemented involve using expensive camera equipment or a motion capture suit. An image-based swing classification system is proposed and evaluated on the GolfDB dataset. The system implements an automated golfer detector combined with traditional machine learning algorithms and a CNN to classify swing events.
The best performing classifier, the LinearSVM, achieved a recall score of 88.3% on the entire GolfDB dataset when combined with the golfer detector. However, without golfer detection, the pruned VGGNet achieved a recall score of 87.9%, significantly better (>10.7%) than the traditional machine learning models. The results are promising as the proposed system outperformed a Bi-LSTM deep learning approach to achieve swing sequencing, which achieved a recall score of 76.1% on the same GolfDB dataset. Overall, the results were promising and worked towards a system that can assist all golfers in swing sequencing without expensive equipment.
This study was funded by the National Research Foundation of South Africa. This work was undertaken in the Distributed Multimedia CoE at Rhodes University.
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Notes
- 1.
PCE closely relates to the recall, the ratio of the number of true positives to the combined number of true positives and false negatives, metric used to measure machine learning models performance.
- 2.
Bi-LSTM models fall into the category of Bidirectional Recurrent Neural Networks [18].
- 3.
An array containing ten items each corresponding to the frame of an event, [SF, A, TU, MB, T, MD, I, MFT, F, EF].
- 4.
Data from outside the training dataset is used to create the model, sharing information between the validation and training data sets [23, p. 93].
- 5.
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Marais, M., Brown, D. (2022). Golf Swing Sequencing Using Computer Vision. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., Sánchez, J.A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2022. Lecture Notes in Computer Science, vol 13256. Springer, Cham. https://doi.org/10.1007/978-3-031-04881-4_28
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