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Related Experiment Video

Updated: Jul 16, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

A novel node and edge cyclic embedding graph convolutional network for skeleton-based two-person interaction

Donghui Wu1, Guozhi Liu1, Dasong Guan1

  • 1College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, Henan, China.

Scientific Reports
|July 14, 2026
PubMed
Summary

This study introduces a novel Graph Convolutional Network (GCN) model for recognizing two-person interactions using skeleton data. The new method effectively captures subtle interactive cues, significantly improving recognition accuracy for social behavior analysis.

Keywords:
Graph convolutional networks (GCN)Human activity recognitionSkeleton-based action recognitionTwo-person interaction recognition

Related Experiment Videos

Last Updated: Jul 16, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human Behavior Analysis

Background:

  • Accurate recognition of two-person interactions is crucial for social behavior analysis and public safety.
  • Existing methods using human skeleton data struggle to effectively capture interactive information in dyadic actions.
  • Graph Convolutional Networks (GCNs) show promise for skeleton data but often overlook interdependent node and edge features in interactions.

Purpose of the Study:

  • To develop an advanced GCN-based method for accurate two-person interaction recognition.
  • To address the challenge of effectively capturing rich interactive information between individuals.
  • To improve the modeling of temporal dynamics and key features in coordinated body movements.

Main Methods:

  • Proposed a Node and Edge Cyclic Embedding Graph Convolutional Network (NECEGCN) for two-person interaction recognition.
  • Introduced a Tri-Graph Cyclic Block to capture inherent interactive information by considering node and edge features holistically.
  • Employed a Multi-Scale Temporal Convolution Block for effective temporal modeling of skeleton sequences and a Tri-Graph Attention Block to highlight key interaction features.

Main Results:

  • Achieved high accuracy of 99.4±0.2% on the SBU-Kinect dataset.
  • Obtained competitive recognition accuracies on NTU RGB+D 60 (95.2±0.2%, 97.6±0.2%) and NTU RGB+D 120 (90.7±0.3%, 90.8±0.2%) datasets under various protocols.
  • Demonstrated superior performance compared to existing methods in recognizing two-person interactions.

Conclusions:

  • The proposed NECEGCN method effectively captures crucial interactive information for accurate two-person interaction recognition.
  • The integration of Tri-Graph Cyclic and Attention Blocks enhances the modeling of dyadic actions and coordinated movements.
  • The method offers a significant advancement in skeleton-based human interaction analysis.