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An adaptively multi-correlations aggregation network for skeleton-based motion recognition.

Xinpeng Yin1, Jianqi Zhong1, Deliang Lian1

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Summary
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This study introduces Adaptively Multi-correlations Aggregation Network (AMANet) for 3D skeleton-based motion recognition. AMANet effectively models dynamic joint dependencies, improving recognition accuracy by capturing non-connected relationships in human poses.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Graph Convolutional Networks (GCNs) show promise in 3D skeleton-based motion recognition.
  • Existing methods often overlook dynamic correlations between human joints and non-connected skeletal relationships.

Purpose of the Study:

  • To propose a novel network, Adaptively Multi-correlations Aggregation Network (AMANet), for enhanced 3D skeleton-based motion recognition.
  • To effectively model dynamic joint dependencies and capture information from non-connected skeletal structures.

Main Methods:

  • Introduced AMANet with three key modules: Spatial Feature Extraction Module (SFEM), Temporal Feature Extraction Module (TFEM), and Spatio-Temporal Feature Extraction Module (STFEM).
  • Utilized relative joint coordinates from Differential Geometry's moving frames.
  • Developed a Data Preprocessing Module (DP) to enrich skeleton data characteristics.

Main Results:

  • Demonstrated the effectiveness of AMANet on three public datasets: NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics-Skeleton 400.
  • The proposed method successfully captures dynamic joint dependencies crucial for accurate motion recognition.

Conclusions:

  • AMANet offers a significant advancement in 3D skeleton-based motion recognition by addressing limitations of existing GCN-based approaches.
  • The method's ability to model dynamic correlations and non-connected skeletal information leads to improved performance.