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Interactive semantics neural networks for skeleton-based human interaction recognition
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DOI：10.1007/s00371-024-03420-4.png)
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Skeleton-based human interaction recognition is a formidable challenge that demands the capability to discern spatial, temporal, and interactive features. However, current research still faces some limitations in identifying spatial, temporal, and interaction features. Methods based on graph convolutional networks often prove to be insufficient in capturing interactive features and structural semantic information of skeletons. In order to solve this problem, we construct a Mutual-semantic Adjacency Matrix (MAM) by amalgamating the relative semantic attention of two skeleton sequences. This MAM was then integrated with the convolution of residual graphs to enhance the extraction of spatial and interaction features. We propose a novel interactive semantics neural network (ISNN) for skeleton-based human interaction recognition to hierarchically fuse MAM and structural semantic information. In addition, integrating the bone stream, we propose a two-stream Interactive Semantics Neural Network (2 s-ISNN). Experiments conducted with our models on two interaction datasets, NTU-RGB+D (mutual) and NTU-RGB+D 120 (mutual), demonstrate significantly improved recognition capabilities in comprehending human interactions. The source code is available at: https://github.com/czant1977/ISNN-master//.
Keyword:
Skeleton-based interaction recognition
Action recognition
Graph convolutional network
Semantic information
Hierarchical network
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2.9
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4.6K
被引数:
6.5K
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