| WU Jin(吴 进),WANG Lei,FENG Haoran,CHONG Gege.[J].高技术通讯(英文),2023,29(4):397~405 |
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| Two stream skeleton behavior recognition algorithmbased on Motif-GCN |
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| DOI： |
| 中文关键词: |
| 英文关键词: skeleton behavior recognition,Motif-GCN,two stream network |
| 基金项目: |
| Author Name | Affiliation | | WU Jin(吴 进) | (School of Electronic Engineering,Xi′an University of Posts and Telecommunications,Xi′an 710121, P. R. China) | | WANG Lei | | | FENG Haoran | | | CHONG Gege | |
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| 中文摘要: |
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| 英文摘要: |
| Compared with RGB videos and images,human bone data is less vulnerable to external factors
and has stronger robustness. Therefore,behavior recognition methods based on skeletons are widely
studied. Because graph convolution network(GCN) can deal with the irregular topology data of human
skeletons very well,more and more researchers apply GCN to human behavior recognition. Traditional
graph convolution methods only consider the joints with physical connectivity or the same
type when building the behavior recognition model based on human skeletons structure,which cannot
capture higher-order information better. To solve this problem,Motif-GCN is used in this paper to extract
spatial features. The relationship between the joints with natural connection in the human body
is encoded by the first Motif-GCN ,and the possible relationship between the unconnected joints in
the human skeleton is encoded by the second Motif-GCN. In this way,the relationship between nonphysical
joints can be strengthened. Then a two stream framework combining joint and bone information
is used to capture more action information. Finally,experiments are conducted on two subdatasets
X-Sub and X-View of NTU-RGB + D,and the accuracy shown in Top-1 classification results is
89. 5% and 95. 4% respectively. The experimental results are 1. 0% and 0. 3% higher than those of
the 2S-AGCN model respectively. The superiority of this method is also proved by the experimental
results. |
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