Abstract
Calisthenics is a fast-growing bodyweight discipline that consists of different categories, one of which is focused on skills. Skills in calisthenics encompass both static and dynamic elements performed by athletes. The evaluation of static skills is based on their difficulty level and the duration of the hold. Automated tools able to recognize isometric skills from a video by segmenting them to estimate their duration would be desirable to assist athletes in their training and judges during competitions. Although the video understanding literature on action recognition through body pose analysis is rich, no previous work has specifically addressed the problem of calisthenics skill temporal video segmentation. This study aims to provide an initial step towards the implementation of automated tools within the field of Calisthenics. To advance knowledge in this context, we propose a dataset of video footage of static calisthenics skills performed by athletes. Each video is annotated with a temporal segmentation which determines the extent of each skill. We hence report the results of a baseline approach to address the problem of skill temporal segmentation on the proposed dataset. The results highlight the feasibility of the proposed problem, while there is still room for improvement.
Abstract (translated)
体操健身（Calisthenics）是一种快速增长的自重训练学科，它包含不同的类别，其中一个重点是技能。在体操健身中，技能包括运动员执行的各种静态和动态元素。静态技能的评估基于它们的难度等级以及保持的时间长度。能够通过分析视频中的身体姿态来识别等长（静止）技能并估算其持续时间的自动化工具对运动员训练和裁判评判比赛时非常有用。 尽管关于动作识别的身体姿态分析文献丰富，但此前没有研究专门解决体操健身技能在视频中进行时间分割的问题。本研究旨在为实现体操健身领域的自动化工具提供初步步骤。为了在这个领域推进知识，我们提出了一套由运动员表演的静态体操健身技巧的视频数据集。每个视频都标注了时间分割信息，以确定每项技能的具体范围。因此，我们报告了一个基本方法的结果，用于解决在提议的数据集中进行技能时间分割的问题。 结果表明，所提问题的实现是可行的，但仍有改进的空间。
URL
https://arxiv.org/abs/2507.12245
