Part-aligned pose-guided recurrent network for action recognition | |
Huang, Linjiang1,2![]() ![]() ![]() | |
发表期刊 | PATTERN RECOGNITION
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ISSN | 0031-3203 |
2019-08-01 | |
卷号 | 92期号:1页码:165-176 |
摘要 | Action recognition using pose information has drawn much attention recently. However, most previous approaches treat human pose as a whole or just use pose to extract robust features. Actually, human body parts play an important role in action, and so modeling spatio-temporal information of body parts can effectively assist in classifying actions. In this paper, we propose a Part-aligned Pose-guided Recurrent Network (P2RN) for action recognition. The model mainly consists of two modules, i.e., part alignment module and part pooling module, which are used for part representation learning and part-related feature fusion, respectively. The part-alignment module incorporates an auto-transformer attention, aiming to capture spatial configuration of body parts and predict pose attention maps. While the part pooling module exploits both symmetry and complementarity of body parts to produce fused body representation. The whole network is a recurrent network which can exploit the body representation and simultaneously model spatio-temporal evolutions of human body parts. Experiments on two publicly available benchmark datasets show the state-of-the-art performance and demonstrate the power of the two proposed modules. |
关键词 | Action recognition Part alignment Auto-transformer attention |
DOI | 10.1016/j.patcog.2019.03,010 |
关键词[WOS] | Action Recognition ; Human Pose ; Part Alignment |
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[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61806194] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Beijing Science and Technology Project[Z181100008918010] ; National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61806194] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Beijing Science and Technology Project[Z181100008918010] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000468013000014 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/24249 |
专题 | 模式识别实验室 |
通讯作者 | Wang, Liang |
作者单位 | 1.University of Chinese Academy of Sciences 2.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition 3.Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences 4.University of Sydney |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Huang, Linjiang,Huang, Yan,Ouyang, Wanli,et al. Part-aligned pose-guided recurrent network for action recognition[J]. PATTERN RECOGNITION,2019,92(1):165-176. |
APA | Huang, Linjiang,Huang, Yan,Ouyang, Wanli,&Wang, Liang.(2019).Part-aligned pose-guided recurrent network for action recognition.PATTERN RECOGNITION,92(1),165-176. |
MLA | Huang, Linjiang,et al."Part-aligned pose-guided recurrent network for action recognition".PATTERN RECOGNITION 92.1(2019):165-176. |
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