SkateboardAI: The Coolest Video Action Recognition for Skateboarding (Student Abstract)


  • Hanxiao Chen Harbin Institute of Technology



Action Recognition, Video Classification, Skateboarding, Transformer, Multi-modal Learning


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.




How to Cite

Chen, H. (2023). SkateboardAI: The Coolest Video Action Recognition for Skateboarding (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16184-16185.