Lightweight graph convolutional network with fusion data for skeleton based action recognition

Qixiang Sun; Ning He; Ren Zhang; Haigang Yu; Shengjie Liu

doi:10.1117/12.2643893

12 October 2022 Lightweight graph convolutional network with fusion data for skeleton based action recognition

Qixiang Sun, Ning He, Ren Zhang, Haigang Yu, Shengjie Liu

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123420R (2022) https://doi.org/10.1117/12.2643893
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Action recognition methods based on human skeletons can clearly express human actions. We present a lightweight graph convolutional network with various streams of data, given the network’s computational complexity and high computational cost of current mainstream human action recognition networks. First, four characteristic data streams are fused using a multi-stream data fusion algorithm, and the best result can be produced with only one training session, minimizing the network’s computational complexity. Second, a non-local graph convolution module based on the graph convolutional network is designed to collect the image’s global information and increase action recognition accuracy. Finally, the spatial Ghost graph convolution module and the temporal Ghost graph convolution module are intended to minimize the network’s computational complexity even more. On the action recognition datasets NTU60 RGB+D and NTU120 RGB+D dataset Our methods achieve highly competitive performance, with average precision of 96.4 and 87.5 percent respectively.

Citation Download Citation

Qixiang Sun, Ning He, Ren Zhang, Haigang Yu, and Shengjie Liu "Lightweight graph convolutional network with fusion data for skeleton based action recognition", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123420R (12 October 2022); https://doi.org/10.1117/12.2643893

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KEYWORDS

Convolution

Data fusion

Network architectures

Bone

Cameras

Detection and tracking algorithms

Neural networks

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