mlsanityData to 14 Oct 2026

Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)

arXiv 2301.13360 ↗First posted 31 Jan 2023cs.CV cs.AIcited by 4

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Abstract (arXiv)

Recently, there has been a remarkable increase in the interest towards skeleton-based action recognition within the research community, owing to its various advantageous features, including computational efficiency, representative features, and illumination invariance. Despite this, researchers continue to explore and investigate the most optimal way to represent human actions through skeleton representation and the extracted features. As a result, the growth and availability of human action recognition datasets have risen substantially. In addition, deep learning-based algorithms have gained widespread popularity due to the remarkable advancements in various computer vision tasks. Most state-of-the-art contributions in skeleton-based action recognition incorporate a Graph Neural Network (GCN) architecture for representing the human body and extracting features. Our research demonstrates that Convolutional Neural Networks (CNNs) can attain comparable results to GCN, provided that the proper training techniques, augmentations, and optimizers are applied. Our approach has been rigorously validated, and we have achieved a score of 95% on the NTU-60 dataset

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 28 of its 52 references that are in the map. First posted on arXiv 31 Jan 2023.

Builds on

28 of 52 references.

References · 28

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16 more
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Cited by year

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

4 papers.

  1. 2024Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patchescited by 22
  2. 2024TASAR: Transfer-based Attack on Skeletal Action Recognitioncited by 7
  3. 2025Read the Room: Inferring Social Context Through Dyadic Interaction Recognition in Cyber-physical-social Infrastructure Systemscited by 1
  4. 2024Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Spacecited by 0