Multi-Scale Spatial Temporal Graph Neural Network for Skeleton-Based Action Recognition
文献类型：期刊论文
| 作者 | Feng, Dong1,2,3; Wu, ZhongCheng1,2 ; Zhang, Jun1,3; Ren, TingTing1,3
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| 刊名 | IEEE ACCESS
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| 出版日期 | 2021 |
| 卷号 | 9 |
| 关键词 | Skeleton-based action recognition multi-scale spatial-temporal network graph convolutional network adaptive fusion |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2021.3073107 |
| 通讯作者 | Ren, TingTing(ttren@hmfl.ac.cn) |
| 英文摘要 | Graph convolutional networks (GCNs) have achieved remarkable performance on skeleton-based action recognition. Existing GCN-based methods usually apply the fixed graph topology and one fixed temporal convolution kernel to extract the spatial features of joints and temporal features, which is from a single-scale perspective. Actually, human actions are coordinated by various body parts in the spatial domain, and exhibit different characteristics in the temporal domain. Therefore, it is appropriate to model the multi-scale information that can enhance both the explainability and stability, which is ignored in current literatures. To address this issue, we propose a multi-scale spatial-temporal graph neural network (MSTGNN) to discover multi-scale discriminative features from spatial and temporal aspects simultaneously. Our contributions are three-folds: 1) For the spatial domain, inspired by the kinematics of the human action, we develop a three-scale graph data structures in a fine-to-coarse way. A novel hybrid spatial pooling module is then proposed to dynamically exploit the global and comprehensive information step-by-step. 2) For the temporal domain, we design a multi-scale temporal convolution module adaptively fusing the temporal features extracted by different scale convolution kernels. 3) As utilizing one-stream architecture instead of multi-stream architecture, the proposed model can be trained in an end-to-end manner. MSTGNN achieves state-of-the-art performance with less computation complexity. Experimental results conducted on two large datasets (NTU-RGB+D and NTU-RGB+D-120) demonstrate the superiority of MSTGNN. |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000641944500001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/121569]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Ren, TingTing |
| 作者单位 | 1.Chinese Acad Sci, Hefei Inst Phys Sci, High Magnet Field Lab, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Sch Hefei Inst Phys Sci, Hefei 230031, Peoples R China 3.High Magnet Field Lab Anhui Prov, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Feng, Dong,Wu, ZhongCheng,Zhang, Jun,et al. Multi-Scale Spatial Temporal Graph Neural Network for Skeleton-Based Action Recognition[J]. IEEE ACCESS,2021,9. |
| APA | Feng, Dong,Wu, ZhongCheng,Zhang, Jun,&Ren, TingTing.(2021).Multi-Scale Spatial Temporal Graph Neural Network for Skeleton-Based Action Recognition.IEEE ACCESS,9. |
| MLA | Feng, Dong,et al."Multi-Scale Spatial Temporal Graph Neural Network for Skeleton-Based Action Recognition".IEEE ACCESS 9(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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