Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM-Reference-Cited by-同舟云学术

Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Kim Hyunsung¹^ORCID,Choi Han-Jun²^ORCID,Kim Chang Jo¹^ORCID,Yoon Jinsung¹^ORCID,Ko Sang-Ki³^ORCID

Affiliation:

1. Fitogether Inc., Seoul, Republic of Korea

2. Kangwon National University, Chuncheon, Republic of Korea

3. Kangwon National University & Fitogether Inc., Chuncheon, Republic of Korea

Funder

National Research Foundation of Korea

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599779

Reference54 articles.

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3. Association of match running performance with and without ball possession to football performance

4. W. Cao , D. Wang , J. Li , H. Zhou , L. Li , and Y. Li . BRITS: Bidirectional recurrent imputation for time series . In Advances in Neural Information Processing Systems , 2018 . W. Cao, D. Wang, J. Li, H. Zhou, L. Li, and Y. Li. BRITS: Bidirectional recurrent imputation for time series. In Advances in Neural Information Processing Systems, 2018.

5. Catapult Sports. https://www.catapultsports.com. Accessed: 2023-02-02. Catapult Sports. https://www.catapultsports.com. Accessed: 2023-02-02.

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