# Calisthenics Skills Temporal Video Segmentation

arXiv: https://arxiv.org/abs/2507.12245 · HTML: https://mlsanity.com/paper/2507.12245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Temporal Action Segmentation: An Analysis of Modern Techniques](https://mlsanity.com/paper/2210.10352/) (2022)
- [A Survey on 3D Skeleton-Based Action Recognition Using Learning Method](https://mlsanity.com/paper/2002.05907/) (2020)
- [DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations](https://mlsanity.com/paper/2208.08190/) (2022)
- [SoccerNet 2022 Challenges Results](https://mlsanity.com/paper/2210.02365/) (2022)
- [Detecting events and key actors in multi-person videos](https://mlsanity.com/paper/1511.02917/) (2015)
- [Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation](https://mlsanity.com/paper/2104.09907/) (2021)
- [Multi-Modal Trajectory Prediction of NBA Players](https://mlsanity.com/paper/2008.07870/) (2020)
- [Contrastive Learning for Sports Video: Unsupervised Player Classification](https://mlsanity.com/paper/2104.10068/) (2021)
- [Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning](https://mlsanity.com/paper/2101.05388/) (2021)
- [Detecting and Matching Related Objects with One Proposal Multiple Predictions](https://mlsanity.com/paper/2104.12574/) (2021)
- [Automated Tackle Injury Risk Assessment in Contact-Based Sports -- A Rugby Union Example](https://mlsanity.com/paper/2104.10916/) (2021)
- [DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera](https://mlsanity.com/paper/2105.09880/) (2021)

## Abstract (arXiv)

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.