CASIA OpenIR
Part-aligned pose-guided recurrent network for action recognition
Huang, Linjiang1,2; Huang, Yan1,2; Ouyang, Wanli4; Wang, Liang1,2,3
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2019-08-01
Volume92Pages:165-176
Corresponding AuthorWang, Liang(wangliang@nlpr.ia.ac.cn)
AbstractAction 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 ((PRN)-R-2) 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. (C) 2019 Elsevier Ltd. All rights reserved.
KeywordAction recognition Part alignment Auto-transformer attention
DOI10.1016/j.patcog.2019.03,010
WOS KeywordREPRESENTATION ; HISTOGRAMS
Indexed BySCI
Language英语
Funding ProjectNational 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[61721004] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61806194] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Beijing Science and Technology Project[Z181100008918010]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Capital Science and Technology Leading Talent Training Project ; Beijing Science and Technology Project
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000468013000014
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24249
Collection中国科学院自动化研究所
Corresponding AuthorWang, Liang
Affiliation1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.NLPR, CRIPAC, Beijing, Peoples R China
3.Chinese Acad Sci CASIA, Inst Automat, Ctr Excellence Brain Sci & Intelligence Technol C, Beijing, Peoples R China
4.Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW, Australia
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Huang, Linjiang,Huang, Yan,Ouyang, Wanli,et al. Part-aligned pose-guided recurrent network for action recognition[J]. PATTERN RECOGNITION,2019,92:165-176.
APA Huang, Linjiang,Huang, Yan,Ouyang, Wanli,&Wang, Liang.(2019).Part-aligned pose-guided recurrent network for action recognition.PATTERN RECOGNITION,92,165-176.
MLA Huang, Linjiang,et al."Part-aligned pose-guided recurrent network for action recognition".PATTERN RECOGNITION 92(2019):165-176.
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