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An efficient self-attention network for skeleton-based action recognition.

Xiaofei Qin¹, Rui Cai¹, Jiabin Yu^2,3

¹School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Scientific Reports

|March 9, 2022

Summary

This summary is machine-generated.

This study introduces a novel network for skeleton-based action recognition, enhancing performance by dynamically optimizing graph structures and enriching data with bone information. The method achieves high accuracy with efficient computation, advancing the field of human pose estimation.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Skeleton-based action recognition is crucial for understanding human behavior.
Graph Convolutional Networks (GCNs) are the dominant approach, often optimizing skeleton graph structures.
Existing methods focus on static graph structures, limiting adaptability.

Purpose of the Study:

To propose a simple yet powerful network for skeleton-based action recognition.
To dynamically optimize skeleton graph structures using self-attention mechanisms.
To improve performance by enriching skeleton data with bone connection information.

Main Methods:

Designed self-attention modules to exploit spatial-temporal dependencies and adapt graph structures.
Developed a lightweight and efficient network architecture tailored for skeleton data.
Introduced a data enrichment technique using bone connection information.

Main Results:

Achieved 90.5% accuracy on the NTU60 cross-subjects dataset.
The proposed network has only 0.89M parameters.
Demonstrated a computation cost of 0.32 GMACs.

Conclusions:

The novel network effectively captures spatial-temporal dependencies for action recognition.
Dynamic graph optimization and bone information enrichment significantly boost performance.
The proposed method offers a strong balance between accuracy and computational efficiency.