Multi-modality dense graph convolution network for skeleton-based action recognition

Dengdi Sun; Yu Guo; Bin Luo; Zhuanlian Ding

doi:10.1117/12.2684941

19 October 2023 Multi-modality dense graph convolution network for skeleton-based action recognition

Dengdi Sun, Yu Guo, Bin Luo, Zhuanlian Ding

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090Q (2023) https://doi.org/10.1117/12.2684941
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Skeleton-based human action recognition has emerged as a vibrant area of research in recent years. In action recognition based on skeleton, the method based on graph convolution network is through modeling space to explore physical dependence between body joints, has obtained the remarkable performance, however, most GCN method uses the stratification to aggregate a wide range of field information, which makes the joint characteristics in long distance transmission is weakened. In this paper, dense graph convolutional networks are proposed to overcome these shortcomings. To enhance the informative skeleton features and create a compact representation at an early fusion stage, an MMF is developed. Then, our proposed method utilizes a dense graph convolution operation to improve the local context information of each joint. Finally, feature channels and action classes are specific, In our model, it is crucial to assign weights to important features and focus on the relevant information. Therefore, to improve the identification of features in our model, we introduce a channel joint attention module. A large number of experiments on three large datasets (NTU-RGB+D, NTU-RGB+D120 and Kinetics) have demonstrated the performance of the proposed MMDGCN for skeleton-based action recognition.

Citation Download Citation

Dengdi Sun, Yu Guo, Bin Luo, and Zhuanlian Ding "Multi-modality dense graph convolution network for skeleton-based action recognition", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090Q (19 October 2023); https://doi.org/10.1117/12.2684941

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KEYWORDS

Convolution

Action recognition

RGB color model

Data modeling

Performance modeling

Motion models

3D modeling

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