Calisthenics Skills Temporal Video Segmentation

TL;DR

This paper introduces a new dataset and baseline approach for automated temporal segmentation of static calisthenics skills in videos, aiming to assist athletes and judges by recognizing skill durations.

Contribution

It presents the first dataset with annotated calisthenics static skills and provides a baseline method for skill segmentation in videos.

Findings

Feasibility of calisthenics skill segmentation demonstrated.

Baseline approach achieves initial results, indicating room for improvement.

Dataset enables future research in automated calisthenics analysis.

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.