SkateboardAI: The coolest video action recognition for skateboarding (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. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | technical sports technical and natural sciences |
| Tagging: | künstliche Intelligenz |
| Published in: | Proceedings of the AAAI Conference on Artificial Intelligence |
| Language: | English |
| Published: |
2025
|
| Online Access: | https://doi.org/10.1609/aaai.v37i13.26952 |
| Volume: | 37 |
| Issue: | 13 |
| Pages: | 16184-16185 |
| Document types: | article |
| Level: | advanced |