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This review focuses on skeleton graph-neural network (GNN) methods for human action recognition. It introduces a new taxonomy and discusses datasets, code, and future research directions for this emerging field.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human action recognition is crucial for applications like video surveillance and human-computer interaction.
  • Existing reviews seldom focus on skeleton-graph-based approaches, despite their natural representation of human pose.
  • Skeleton graphs, formed by connecting human skeleton joints, offer a promising avenue for action recognition.

Purpose of the Study:

  • To provide an up-to-date review of skeleton graph-neural network (GNN)-based human action recognition methods.
  • To propose a novel taxonomy for classifying skeleton-GNN approaches based on their design.
  • To analyze the strengths and weaknesses of existing methods.

Main Methods:

  • Systematic review of existing literature on skeleton-GNN for human action recognition.
  • Development of a new taxonomy to categorize skeleton-GNN methods.
  • Analysis of performance, merits, and demerits of different approaches.

Main Results:

  • Identification of key trends and challenges in skeleton-GNN based action recognition.
  • A structured overview of various skeleton-GNN architectures and their effectiveness.
  • Discussion on relevant datasets and publicly available code for reproducibility.

Conclusions:

  • Skeleton-GNNs represent a significant advancement in human action recognition.
  • The proposed taxonomy provides a valuable framework for understanding and comparing methods.
  • Further research is needed to address limitations and explore new directions in the field.