首个标注攀岩抓握动作的视频数据集，助力智能攀岩分析。
The Way Up: A Dataset for Hold Usage Detection in Sport Climbing
- 基于关键点检测抓握动作，通过关节位置与岩点重合度判断使用。
- 构建22段视频、含岩点位置、使用顺序和时间的标注数据集。
- 揭示攀岩姿态识别难点，适合体育AI与动作分析研究者参考。
在各类攀岩相关应用中，准确判断运动员在路线上的位置及抓握动作至关重要。然而，目前尚无公开的、带有详细抓握标注的攀岩数据集。为此，我们构建了一个包含22段标注视频的数据集，提供岩点位置、使用顺序及使用时间的真值标签。同时，我们探索了基于关键点的2D姿态估计模型在运动攀岩中抓握检测的应用。通过分析特定关节的关键点与岩点的空间重叠情况来判断抓握行为。我们在该数据集上评估了多种前沿姿态估计模型，并分析其性能，揭示了攀岩场景下的独特挑战。本数据集与实验结果凸显了攀岩专用姿态估计的核心难点，为未来智能化攀岩辅助系统的研究奠定了基础。
原文摘要 · Abstract (English)
Detecting an athlete's position on a route and identifying hold usage are crucial in various climbing-related applications. However, no climbing dataset with detailed hold usage annotations exists to our knowledge. To address this issue, we introduce a dataset of 22 annotated climbing videos, providing ground-truth labels for hold locations, usage order, and time of use. Furthermore, we explore the application of keypoint-based 2D pose-estimation models for detecting hold usage in sport climbing. We determine usage by analyzing the key points of certain joints and the corresponding overlap with climbing holds. We evaluate multiple state-of-the-art models and analyze their accuracy on our dataset, identifying and highlighting climbing-specific challenges. Our dataset and results highlight key challenges in climbing-specific pose estimation and establish a foundation for future research toward AI-assisted systems for sports climbing.
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