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Related Experiment Video

Updated: Jul 24, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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TFC-GCN: Lightweight Temporal Feature Cross-Extraction Graph Convolutional Network for Skeleton-Based Action

Kaixuan Wang1, Hongmin Deng1

  • 1College of Electronics and Information Engineering, Sichuan University, No. 24, Section 1, First Ring Road, Wuhou District, Chengdu 610041, China.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Temporal Feature Cross-Extraction Graph Convolutional Network (TFC-GCN) for skeleton-based action recognition. The TFC-GCN model efficiently extracts temporal features and reduces parameters, improving action recognition accuracy.

Keywords:
action recognitiondeep learninggraph convolutional networkslightweight

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Graph Convolutional Networks (GCNs) excel in skeleton-based action recognition.
  • Existing methods often overlook novel input features and temporal dynamics.
  • Many GCN models suffer from large parameter counts, leading to swollen structures.

Purpose of the Study:

  • To propose an efficient and effective GCN model for skeleton-based action recognition.
  • To address limitations in feature extraction and temporal modeling in current approaches.
  • To develop a model with a reduced number of parameters without compromising performance.

Main Methods:

  • Introduced a feature extraction strategy based on the relative displacements of joints across frames.
  • Employed a temporal feature cross-extraction block with gated information filtering to capture high-level action representations.
  • Developed a stitching spatial-temporal attention (SST-Att) block to assign differential weights to joints.

Main Results:

  • The proposed Temporal Feature Cross-Extraction Graph Convolutional Network (TFC-GCN) achieves high performance.
  • TFC-GCN demonstrates a significantly reduced parameter count (0.18 M) and FLOPs (1.90 G).
  • The model's effectiveness was validated on large-scale datasets: NTU RGB+D60, NTU RGB+D120, and UAV-Human.

Conclusions:

  • TFC-GCN offers a superior approach to skeleton-based action recognition by effectively integrating temporal features and spatial information.
  • The model's efficiency in terms of parameters and computation makes it suitable for practical applications.
  • The proposed methods, including relative joint displacement features and SST-Att, contribute to enhanced action recognition accuracy.