A stroke of genius: Predicting the next move in badminton

Ibh, Magnus; Graßhof, Stella; Hansen, Dan Witzner

doi:10.1109/cvprw63382.2024.00342

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2024

DOI: 10.1109/cvprw63382.2024.00342

|View full text |Cite

A stroke of genius: Predicting the next move in badminton

Magnus Ibh,

Stella Graßhof,

Dan Witzner Hansen

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Causal Behaviour Modelling for Player Action Prediction in Rugby League Using Graph Neural Networks

Wang,

Gendron

et al. 2024

2024 39th International Conference on Image and Vision Computing New Zealand (IVCNZ)

View full text Add to dashboard Cite

Causal Behaviour Modelling for Player Action Prediction in Rugby League Using Graph Neural Networks

Wang,

Gendron

et al. 2024

2024 39th International Conference on Image and Vision Computing New Zealand (IVCNZ)

View full text Add to dashboard Cite

3D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approach

Tanaka,

Suzuki,

Fujii

2024

Proceedings of the 7th ACM International Workshop on Multimedia Content Analysis in Sports

View full text Add to dashboard Cite

Understanding human actions from videos is essential in many domains, including sports. In figure skating, technical judgments are performed by watching skaters' 3D movements, and its part of the judging procedure can be regarded as a Temporal Action Segmentation (TAS) task. TAS tasks in figure skating that automatically assign temporal semantics to video are actively researched. However, there is a lack of datasets and effective methods for TAS tasks requiring 3D pose data. In this study, we first created the FS-Jump3D dataset of complex and dynamic figure skating jumps using optical markerless motion capture. We also propose a new fine-grained figure skating jump TAS dataset annotation method with which TAS models can learn jump procedures. In the experimental results, we validated the usefulness of 3D pose features as input and the fine-grained dataset for the TAS model in figure skating. FS-Jump3D Dataset is available at https://github.com/ryota-skating/FS-Jump3D.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A stroke of genius: Predicting the next move in badminton

Cited by 2 publications

References 26 publications

Causal Behaviour Modelling for Player Action Prediction in Rugby League Using Graph Neural Networks

Causal Behaviour Modelling for Player Action Prediction in Rugby League Using Graph Neural Networks

3D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approach

Contact Info

Product

Resources

About