CASIA OpenIR
Action unit detection and key frame selection for human activity prediction
Wang, Haoran1; Yuan, Chunfeng2; Shen, Jifeng3; Yang, Wankou4; Ling, Haibin5
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2018-11-27
Volume318Pages:109-119
Corresponding AuthorWang, Haoran(wanghaoran@ise.neu.edu.cn)
AbstractHuman activity prediction aims to recognize an unfinished activity with limited appearance and motion information. In this paper, we propose to predict an incomplete activity by combining the mid-level action units and the discriminative key frames exploited from each activity class. Specifically, we extract a great deal of action-related volumes from activity videos. Based on a set of low-level powerful features, similar volumes are aggregated into a mid-level feature, named action unit. Then, we detect these action units in each activity video and generate the frame feature by computing the distribution of concurrent action units in a single frame. Notice that human can easily recognize an incomplete activity using scanty key frames composed of representative interrelated action units together. The key frames in each activity class are selected by computing the entropy of each single frame feature. Finally, a structured SVM is trained to recognize activities with different observation ratios. The proposed approach is evaluated on several publicly available datasets in comparison with state-of-the-art approaches. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach. (C) 2018 Published by Elsevier B.V.
KeywordActivity prediction Key frame selection Action unit detection Structured SVM
DOI10.1016/j.neucom.2018.08.037
WOS KeywordACTION RECOGNITION ; TRAJECTORIES ; VIDEOS ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61603080] ; National Natural Science Foundation of China[61701101] ; National Natural Science Foundation of China[61773117] ; Fundamental Research Funds for the Central Universities of China[N160413002] ; Doctor Startup Fund of Liaoning Province[201601019] ; NSF of Jiangsu Province[BK20150470]
Funding OrganizationNational Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities of China ; Doctor Startup Fund of Liaoning Province ; NSF of Jiangsu Province
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000445763500011
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25748
Collection中国科学院自动化研究所
Corresponding AuthorWang, Haoran
Affiliation1.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Peoples R China
4.Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
5.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA
Recommended Citation
GB/T 7714
Wang, Haoran,Yuan, Chunfeng,Shen, Jifeng,et al. Action unit detection and key frame selection for human activity prediction[J]. NEUROCOMPUTING,2018,318:109-119.
APA Wang, Haoran,Yuan, Chunfeng,Shen, Jifeng,Yang, Wankou,&Ling, Haibin.(2018).Action unit detection and key frame selection for human activity prediction.NEUROCOMPUTING,318,109-119.
MLA Wang, Haoran,et al."Action unit detection and key frame selection for human activity prediction".NEUROCOMPUTING 318(2018):109-119.
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