mlsanityData to 14 Oct 2026

SkateboardAI: The Coolest Video Action Recognition for Skateboarding

arXiv 2311.11467 ↗First posted 2 Aug 2023cs.CV cs.AIcited by 4

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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.

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

9 of 12 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
  3. 2018SlowFast Networks for Video Recognitioncited by 1,867
  4. 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,068
  5. 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 902
  6. 2017Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?cited by 551
  7. 2015Beyond Short Snippets: Deep Networks for Video Classificationcited by 233
  8. 2015Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptorscited by 58
  9. 2015Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classificationcited by 21

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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024Multi-modality action recognition based on dual feature shift in vehicle cabin monitoringcited by 8
  2. 2024Color-S4L: Self-supervised Semi-supervised Learning with Image Colorizationcited by 2
  3. 2024Car Damage Detection and Patch-to-Patch Self-supervised Image Alignmentcited by 1
  4. 2025Spatial-Temporal Perception with Causal Inference for Naturalistic Driving Action Recognitioncited by 0