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.
References
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Brown, D., Bradshaw, K.: Deep palmprint recognition with alignment and augmentation of limited training samples. SN Comput. Sci. 3(1), 1–17 (2022)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 886–893 (2005). https://doi.org/10.1109/CVPR.2005.177
Dorogush, A.V., Ershov, V., Gulin, A.: CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363 (2018)
Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002). https://doi.org/10.1016/S0167-9473(01)00065-2
Gehrig, N., Lepetit, V., Fua, P.: Visual golf club tracking for enhanced swing analysis. In: British Machine Vision Conference (BMVC), pp. 1–10 (2003)
Glazier, P., Lamp, P.: Golf science: optimum performance from tee to green. In: Golf Science: Optimum Performance From Tee to Green, chap. The swing. University of Chicago Press (2013)
Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 5(2), 1 (2015)
Hsu, Y.L., Chen, Y.T., Chou, P.H., Kou, Y.C., Chen, Y.C., Su, H.Y.: Golf swing motion detection using an inertial-sensor-based portable instrument. In: 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1–2. IEEE (2016)
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. Adv. Neural. Inf. Process. Syst. 30, 3146–3154 (2017)
Ko, K.-R., Pan, S.B.: CNN and Bi-LSTM based 3D golf swing analysis by frontal swing sequence images. Multimedia Tools Appl. 80(6), 8957–8972 (2020). https://doi.org/10.1007/s11042-020-10096-0
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
McNally, W., Vats, K., Pinto, T., Dulhanty, C., McPhee, J., Wong, A.: GolfDB: a video database for golf swing sequencing. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, June 2019, pp. 2553–2562 (2019). https://doi.org/10.1109/CVPRW.2019.00311
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Schuster, M., Paliwal, K.: Bidirectional recurrent neural networks. IEEE Trans. Sig. Process. 45, 2673–2681 (1997). https://doi.org/10.1109/78.650093
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Smith, A., Roberts, J., Wallace, E., Forrester, S.: Professional golf coaches’ perceptions of the key technical parameters in the golf swing. In: Procedia Engineering, vol. 34, pp. 224–229. Elsevier Ltd., Amsterdam (2012). https://doi.org/10.1016/j.proeng.2012.04.039
Tharwat, A., Gaber, T., Ibrahim, A., Hassanien, A.E.: Linear discriminant analysis: a detailed tutorial. AI Commun. 30(2), 169–190 (2017)
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)
Zheng, A., Casari, A.: Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, 1st edn. O’Reilly Media Inc., Newton (2018)
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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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