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Dyadic relational graph convolutional networks for skeleton-based human interaction recognition
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DOI：10.1016/j.patcog.2021.107920.png)
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Skeleton-based human interaction recognition is a challenging task requiring all abilities to recognize spatial, temporal, and interactive features. These abilities rarely co-exist in existing methods. Graph convolutional network (GCN) based methods fail to extract interactive features. Traditional interaction recognition methods cannot effectively capture spatial features from skeletons. Toward this end, we propose a novel Dyadic Relational Graph Convolutional Network (DR-GCN) for interaction recognition. Specifically, we make four contributions: (i) we design a Relational Adjacency Matrix (RAM) that represents dynamic relational graphs. These graphs are constructed combining both geometric features and relative attention from the two skeleton sequences; (ii) we propose a Dyadic Relational Graph Convolution Block (DR-GCB) that extracts spatial-temporal interactive features; (iii) we stack the proposed DR-GCBs to build DR-GCN and integrate our methods with an advanced model. (iv) Our models achieve state-of-the-art results on SBU and significant improvements on the mutual action sub-datasets of NTU-RGB+D and NTU-RGB+D 120. (c) 2021 Elsevier Ltd. All rights reserved.
Keyword:
3D skeleton-based interaction recognition
Multi-scale graph convolution networks
Graph inference
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7.6
论文数:
1.3W
被引数:
4.5W
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