# SkateboardAI: The Coolest Video Action Recognition for Skateboarding

arXiv: https://arxiv.org/abs/2311.11467 · HTML: https://mlsanity.com/paper/2311.11467/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors](https://mlsanity.com/paper/1505.04868/) (2015)
- [Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification](https://mlsanity.com/paper/1504.01561/) (2015)

## Built on by (most cited first, 4 of 4)

- [Multi-modality action recognition based on dual feature shift in vehicle cabin monitoring](https://mlsanity.com/paper/2401.14838/) (2024)
- [Color-S4L: Self-supervised Semi-supervised Learning with Image Colorization](https://mlsanity.com/paper/2401.03753/) (2024)
- [Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment](https://mlsanity.com/paper/2403.06674/) (2024)
- [Spatial-Temporal Perception with Causal Inference for Naturalistic Driving Action Recognition](https://mlsanity.com/paper/2503.04078/) (2025)

## Abstract (arXiv)

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.