MuLMINet: Multi-Layer Multi-Input Transformer Network with Weighted Loss
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866929304375394304 |
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| author | Seong, Minwoo Oh, Jeongseok Kim, SeungJun |
| author_facet | Seong, Minwoo Oh, Jeongseok Kim, SeungJun |
| contents | The increasing use of artificial intelligence (AI) technology in turn-based sports, such as badminton, has sparked significant interest in evaluating strategies through the analysis of match video data. Predicting future shots based on past ones plays a vital role in coaching and strategic planning. In this study, we present a Multi-Layer Multi-Input Transformer Network (MuLMINet) that leverages professional badminton player match data to accurately predict future shot types and area coordinates. Our approach resulted in achieving the runner-up (2nd place) in the IJCAI CoachAI Badminton Challenge 2023, Track 2. To facilitate further research, we have made our code publicly accessible online, contributing to the broader research community's knowledge and advancements in the field of AI-assisted sports analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_08262 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MuLMINet: Multi-Layer Multi-Input Transformer Network with Weighted Loss Seong, Minwoo Oh, Jeongseok Kim, SeungJun Artificial Intelligence The increasing use of artificial intelligence (AI) technology in turn-based sports, such as badminton, has sparked significant interest in evaluating strategies through the analysis of match video data. Predicting future shots based on past ones plays a vital role in coaching and strategic planning. In this study, we present a Multi-Layer Multi-Input Transformer Network (MuLMINet) that leverages professional badminton player match data to accurately predict future shot types and area coordinates. Our approach resulted in achieving the runner-up (2nd place) in the IJCAI CoachAI Badminton Challenge 2023, Track 2. To facilitate further research, we have made our code publicly accessible online, contributing to the broader research community's knowledge and advancements in the field of AI-assisted sports analysis. |
| title | MuLMINet: Multi-Layer Multi-Input Transformer Network with Weighted Loss |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2307.08262 |