TITLE:
On the Utility of Pose Estimation Models for Golf Swing Understanding
AUTHORS:
Alina Yuan, Bryant Ndongmo
KEYWORDS:
Pose Estimation, Golf Swing Analysis, Computer Vision, YOLO Pose, MediaPipe Pose, Sports Analytics, OKS Metric, Human Motion Analysis
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.14 No.1,
December
22,
2025
ABSTRACT: Human pose estimation has shown increasing potential in sports analytics, particularly for evaluating and improving athletic motion. However, existing models are typically optimized for general-purpose or stationary scenarios and struggle when applied to high-speed, occlusion-prone sports such as golf. This study investigates the performance of two state-of-the-art pose estimation models—YOLO Pose and MediaPipe Pose—in analyzing golf swings from video data. A custom dataset was developed, consisting of golf swing recordings across diverse players, backgrounds, and lighting conditions. Each video was segmented into frames, and a subset was manually annotated to create ground-truth keypoints for evaluation. Model performance was assessed using the Object Keypoint Similarity (OKS) metric. Results show that while MediaPipe Pose achieved higher average accuracy (mean OKS = 0.636) compared to YOLO Pose (mean OKS = 0.604), YOLO demonstrated more consistent predictions with lower variance. Qualitative analysis further revealed that MediaPipe better handles partial occlusions but is more sensitive to environmental factors. These findings highlight trade-offs between model precision, consistency, and robustness in dynamic sports contexts, suggesting the need for domain-specific adaptations to improve accuracy in golf swing analysis. Such insights further underscore how pose-based motion understanding can serve as a foundation for developing intelligent feedback systems, bridging the gap between traditional coaching and automated performance analytics.