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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.

Bibliographic Details
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