Trajectory-based Badminton Shots Detection

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

*Corresponding author for this work

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面64-71
頁數8
ISBN(電子)9780738142623
DOIs
出版狀態Published - 十二月 2020
事件1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
持續時間: 3 十二月 20205 十二月 2020

出版系列

名字Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
國家Taiwan
城市Taipei
期間3/12/205/12/20

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