A lightweight graph convolutional network for skeleton-based action recognition

Pham, Dinh‐Tan; Pham, Quang-Tien; Nguyen, Tien‐Thanh; Le, Thi‐Lan; Vu, Hai

doi:10.1007/s11042-022-13298-w

Multimed Tools Appl

2022

DOI: 10.1007/s11042-022-13298-w

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A lightweight graph convolutional network for skeleton-based action recognition

Dinh‐Tan Pham

Quang-Tien Pham

Tien‐Thanh Nguyen

et al.

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Shuffle Graph Convolutional Network for Skeleton-Based Action Recognition

Yu,

Dai,

Hirota

et al. 2023

JACIII

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A shuffle graph convolutional network (Shuffle-GCN) is proposed to recognize human action by analyzing skeleton data. It uses channel split and channel shuffle operations to process multi-feature channels of skeleton data, which reduces the computational cost of graph convolution operation. Compared with the classical two-stream adaptive graph convolutional network model, the proposed method achieves a higher precision with 1/3 of the floating-point operations (FLOPs). Even more, a channel-level topology modeling method is designed to extract more motion information of human skeleton by learning the graph topology from different channels dynamically. The performance of Shuffle-GCN is tested under 56,880 action clips from the NTU RGB+D dataset with the accuracy 96.0% and the computational complexity 12.8 GFLOPs. The proposed method offers feasible solutions for developing practical applications of action recognition.

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Shuffle Graph Convolutional Network for Skeleton-Based Action Recognition

Yu,

Dai,

Hirota

et al. 2023

JACIII

View full text Add to dashboard Cite

show abstract

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A lightweight graph convolutional network for skeleton-based action recognition

Cited by 1 publication

References 37 publications

Shuffle Graph Convolutional Network for Skeleton-Based Action Recognition

Shuffle Graph Convolutional Network for Skeleton-Based Action Recognition

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