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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2508.20491 |
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| _version_ | 1866912557690781696 |
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| author | Jung, Seunghyeon Hong, Seoyoung Jeong, Jiwoo Jeong, Seungwon Choi, Jaerim Kim, Hoki Lee, Woojin |
| author_facet | Jung, Seunghyeon Hong, Seoyoung Jeong, Jiwoo Jeong, Seungwon Choi, Jaerim Kim, Hoki Lee, Woojin |
| contents | Recent advances in deep learning have led to more studies to enhance golfers' shot precision. However, these existing studies have not quantitatively established the relationship between swing posture and ball trajectory, limiting their ability to provide golfers with the necessary insights for swing improvement. In this paper, we propose a new dataset called CaddieSet, which includes joint information and various ball information from a single shot. CaddieSet extracts joint information from a single swing video by segmenting it into eight swing phases using a computer vision-based approach. Furthermore, based on expert golf domain knowledge, we define 15 key metrics that influence a golf swing, enabling the interpretation of swing outcomes through swing-related features. Through experiments, we demonstrated the feasibility of CaddieSet for predicting ball trajectories using various benchmarks. In particular, we focus on interpretable models among several benchmarks and verify that swing feedback using our joint features is quantitatively consistent with established domain knowledge. This work is expected to offer new insight into golf swing analysis for both academia and the sports industry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CaddieSet: A Golf Swing Dataset with Human Joint Features and Ball Information Jung, Seunghyeon Hong, Seoyoung Jeong, Jiwoo Jeong, Seungwon Choi, Jaerim Kim, Hoki Lee, Woojin Computer Vision and Pattern Recognition Artificial Intelligence Recent advances in deep learning have led to more studies to enhance golfers' shot precision. However, these existing studies have not quantitatively established the relationship between swing posture and ball trajectory, limiting their ability to provide golfers with the necessary insights for swing improvement. In this paper, we propose a new dataset called CaddieSet, which includes joint information and various ball information from a single shot. CaddieSet extracts joint information from a single swing video by segmenting it into eight swing phases using a computer vision-based approach. Furthermore, based on expert golf domain knowledge, we define 15 key metrics that influence a golf swing, enabling the interpretation of swing outcomes through swing-related features. Through experiments, we demonstrated the feasibility of CaddieSet for predicting ball trajectories using various benchmarks. In particular, we focus on interpretable models among several benchmarks and verify that swing feedback using our joint features is quantitatively consistent with established domain knowledge. This work is expected to offer new insight into golf swing analysis for both academia and the sports industry. |
| title | CaddieSet: A Golf Swing Dataset with Human Joint Features and Ball Information |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2508.20491 |