Trajectory-based Badminton Shots Detection

Nyan Ping Ju, Dung Ru Yu, Tsi Ui Ik*, Wen Chih Peng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Shot-by-shot match video segmentation is essential in video-based microscopic data annotation and collection for strategic analysis. With the help of deep learning vision technology, the shuttlecock trajectory can be depicted from broadcast video with accuracy around 78%. In this work, to develop automatic badminton match video labeling, we applied Artificial Neural Networks (ANNs) in the contest strategy data collection to speed up the labeling procedure. The proposed ANN was trained to detect badminton shot events based on shuttlecock trajectories in the contest video. Badminton shot events include serving, hitting, and dead ball. With the help of these shot events, the strategy analyst could annotate strategy information more efficiently and reduce labor costs significantly.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-71
Number of pages8
ISBN (Electronic)9780738142623
DOIs
StatePublished - Dec 2020
Event1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
Duration: 3 Dec 20205 Dec 2020

Publication series

NameProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
CountryTaiwan
CityTaipei
Period3/12/205/12/20

Keywords

  • Artificial-Neural Network
  • Badminton
  • multi-classification
  • polynomial curve fitting
  • shots detection
  • shuttlecock
  • TrackNet
  • trajectory smoothing

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