Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network | |
Si, Chenyang1,2,3![]() ![]() ![]() ![]() ![]() | |
发表期刊 | PATTERN RECOGNITION
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ISSN | 0031-3203 |
2020-11-01 | |
卷号 | 107期号:107511页码:12 |
摘要 | Skeleton-based action recognition aims to recognize human actions by exploring the inherent characteristics from the given skeleton sequences and has attracted far more attention due to its great important potentials in practical applications. Previous methods have illustrated that learning discriminative spatial and temporal features from the skeleton sequences is a crucial factor to recognize human actions. Nevertheless, how to model spatio-temporal evolutions is still a challenging problem. In this work, we propose a novel model with hierarchical spatial reasoning and temporal stack learning network (HSR-TSL) to explore the discriminative spatial and temporal features for human action recognition, which consists of a hierarchical spatial reasoning network (HSRN) and a temporal stack learning network (TSLN). Specifically, the HSRN employs a hierarchical residual graph neural network to capture two-level spatial features: intra spatial information of each part and body-level structural information between each part. The TSLN models the detailed temporal dynamics of skeleton sequences by a composition of multiple skip-clip LSTMs. During training, we develop a clip-based incremental loss to effectively optimize the model. We perform extensive experiments on five challenging benchmarks to verify the effectiveness of each component of our model. The comparison results illustrate that our approach significantly boosts the performances for skeleton-based action recognition. (C) 2020 Elsevier Ltd. All rights reserved. |
关键词 | Skeleton-based action recognition Hierarchical spatial reasoning Temporal stack learning Clip-based incremental loss |
DOI | 10.1016/j.patcog.2020.107511 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61976214] ; National Natural Science Foundation of China[61721004] ; Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project)[2019JZZY010119] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000552866000052 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 生物特征识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40284 |
专题 | 模式识别实验室 |
通讯作者 | Wang, Wei |
作者单位 | 1.Univ Chinese Acad Sci UCAS, Beijing, Peoples R China 2.Natl Lab Pattern Recognit NLPR, Ctr Res Intelligent Percept & Comp CRIPAC, Beijing, Peoples R China 3.Chinese Acad Sci CASIA, Inst Automat, Beijing, Peoples R China |
第一作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Si, Chenyang,Jing, Ya,Wang, Wei,et al. Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network[J]. PATTERN RECOGNITION,2020,107(107511):12. |
APA | Si, Chenyang,Jing, Ya,Wang, Wei,Wang, Liang,&Tan, Tieniu.(2020).Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network.PATTERN RECOGNITION,107(107511),12. |
MLA | Si, Chenyang,et al."Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network".PATTERN RECOGNITION 107.107511(2020):12. |
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