SkateboardAI: The coolest video action recognition for skateboarding (student abstract)
(SkateboardAI: Die coolste Aktionserkennung für Videos beim Skateboarden (Student Abstract) )
Impressed by the coolest skateboarding sports program from 2021 Tokyo Olympic Games, we are the first to curate the original real-world video datasets "SkateboardAI" in the wild, even self-design and implement diverse uni-modal and multi-modal video action recognition approaches to recognize different tricks accurately. For uni-modal methods, we separately apply (1)CNN and LSTM; (2)CNN and BiLSTM; (3)CNN and BiLSTM with effective attention mechanisms; (4)Transformer-based action recognition pipeline. Transferred to the multi-modal conditions, we investigated the two-stream Inflated-3D architecture on "SkateboardAI" datasets to compare its performance with uni-modal cases. In sum, our objective is developing an excellent AI sport referee for the coolest skateboarding competitions.
© Copyright 2025 Proceedings of the AAAI Conference on Artificial Intelligence. Alle Rechte vorbehalten.
| Schlagworte: | |
|---|---|
| Notationen: | technische Sportarten Naturwissenschaften und Technik |
| Tagging: | künstliche Intelligenz |
| Veröffentlicht in: | Proceedings of the AAAI Conference on Artificial Intelligence |
| Sprache: | Englisch |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://doi.org/10.1609/aaai.v37i13.26952 |
| Jahrgang: | 37 |
| Heft: | 13 |
| Seiten: | 16184-16185 |
| Dokumentenarten: | Artikel |
| Level: | hoch |