EMD-GCN: graph convolution network with EM dynamic routing for skeleton-based action recognition

Jianan She; Qian Wang

doi:10.1117/12.2687792

28 August 2023 EMD-GCN: graph convolution network with EM dynamic routing for skeleton-based action recognition

Jianan She, Qian Wang

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272420 (2023) https://doi.org/10.1117/12.2687792
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

Behavior recognition is one of the most popular fields in computer vision, and graph convolution network for skeleton based data has been widely used in the field of action recognition, and has achieved remarkable results. Most of the previous studies focused on modeling the relationship between adjacent joint points in natural state, while ignoring the graph topology between non adjacent joint points. In this work, we propose a new graph convolution with EM dynamic routing to learn different structural features dynamically in action recognition based on human skeleton data, and cluster multiple joint points into corresponding graph topologies effectively. The proposed EMD-GC takes the initially artificially defined shared topology as the general prior knowledge of the model, then models the topology according to the specific correlation of each channel, and finally clusters the features into the corresponding topology through the GMM model. Experiments show that EMD-GCN is superior to the most advanced methods on NTU RGB+D dataset.

Citation Download Citation

Jianan She and Qian Wang "EMD-GCN: graph convolution network with EM dynamic routing for skeleton-based action recognition", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272420 (28 August 2023); https://doi.org/10.1117/12.2687792

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KEYWORDS

Action recognition

Convolution

Matrices

Modeling

Performance modeling

Feature extraction

Bone

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