Human action recognition based on attention mechanism and  two-stream non-local graph convolution

Jianing Li; Yan Piao; Chenkai Ma

doi:10.1117/12.2641472

4 August 2022 Human action recognition based on attention mechanism and two-stream non-local graph convolution

Jianing Li, Yan Piao, Chenkai Ma

Proceedings Volume 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022); 123060Y (2022) https://doi.org/10.1117/12.2641472
Event: Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 2022, Changchun, China

Abstract

Action recognition methods based on human skeleton can explicitly represent human actions, and have gradually become one of the important research directions in the field of computer vision. To address the problems that the skeleton graph in graph convolutional networks is fixed to represent only the physical structure of the human body and the lack of adaptive ability to the skeleton topology graph structure, this paper proposes a dual-stream non-local graph convolutional network based on the attention mechanism. First, the temporal convolution layer is extended to a parallel structure with multiple kernels, and different temporal convolution kernel modules are adaptively selected to collect features by channel weights; second, an attention model consisting of an attention pooling layer is proposed to capture the correlation and temporal continuity among joints; finally, the nonlocal graph convolution network is used as the basic framework with joint information, skeletal information and respective motion information A dual-stream fusion model is constructed. The proposed method is compared with the mainstream methods in recent years on the action recognition dataset NTU RGB+D, and the experimental results show that the proposed method achieves high accuracy in action recognition.

Citation Download Citation

Jianing Li, Yan Piao, and Chenkai Ma "Human action recognition based on attention mechanism and two-stream non-local graph convolution", Proc. SPIE 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 123060Y (4 August 2022); https://doi.org/10.1117/12.2641472

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KEYWORDS

Convolution

Data modeling

Motion models

3D modeling

Cameras

Lithium

Machine vision

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