A stroke of genius: Predicting the next move in badminton

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Abstract

This paper presents a transformer encoder-decoder model for predicting future badminton strokes based on previous rally actions. The model uses court position skeleton poses and player-specific embeddings to learn stroke and player-specific latent representations in a spatiotemporal encoder module. The representations are then used to condition the subsequent strokes in a decoder module through rally-aware fusion blocks which provide additional relevant strategic and technical considerations to make more informed predictions. RallyTemPose shows improved forecasting accuracy compared to traditional sequential methods on two real-world badminton datasets. The performance boost can also be attributed to the inclusion of improved stroke embeddings extracted from the latent representation of a pre-trained large-language model subjected to detailed text descriptions of stroke descriptions. In the discussion the latent representations learned by the encoder module show useful properties regarding player analysis and comparisons.
Original languageEnglish
Title of host publicationProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
Publication dateJun 2024
Pages3376-3385
Publication statusPublished - Jun 2024
EventCVPR 2024 - The IEEE/CVF Conference on Computer Vision and Pattern Recognition - Seattle Convention Center, Seattle, United States
Duration: 17 Jun 202421 Jun 2024
Conference number: 2024
https://cvpr.thecvf.com/

Conference

ConferenceCVPR 2024 - The IEEE/CVF Conference on Computer Vision and Pattern Recognition
Number2024
LocationSeattle Convention Center
Country/TerritoryUnited States
CitySeattle
Period17/06/202421/06/2024
Internet address

Keywords

  • badminton
  • transformer

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