构建首个融合关节与球数据的高尔夫挥杆数据集
CaddieSet: A Golf Swing Dataset with Human Joint Features and Ball Information
- 通过计算机视觉分阶段提取挥杆关节信息
- 定义15项关键指标关联挥杆姿态与球轨迹
- 验证模型反馈与专业领域知识一致，适合运动分析
深度学习进展推动了提升高尔夫击球精度的研究，但现有工作未定量建立挥杆姿势与球轨迹的关系，难以提供有效改进建议。本文提出CaddieSet数据集，包含单次击球的关节信息与多种球数据。通过基于计算机视觉的方法将一次挥杆视频分割为八个阶段，提取关节特征；结合专家领域知识，定义15个影响挥杆的关键指标，实现对击球结果的特征化解释。实验表明，该数据集可支持多种基准模型预测球轨迹，尤其在可解释模型中验证了使用关节特征生成的挥杆反馈与既有领域知识定量一致。本研究为学术界与体育产业提供了新的高尔夫挥杆分析视角。
原文摘要 · Abstract (English)
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.
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