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Human activity prediction using temporally-weighted generalized time warping

文献类型：期刊论文

作者Wang, Haoran1; Yang, Wankou2; Yuan, Chunfeng3; Ling, Haibin4; Hu, Weiming3
刊名NEUROCOMPUTING
出版日期2017-02-15
卷号225期号:1页码:139-147
关键词Activity Prediction Time Warping Alignment
DOI10.1016/j.neucom.2016.11.004
文献子类Article
英文摘要Different from traditional human activity recognition, human activity prediction aims to recognize an unfinished activity, typically in absence of explicit temporal progress status. In this paper, we propose a new human activity prediction approach by extending the recently proposed generalized time warping (GTW) [20], which allows an efficient and flexible alignment of two or more multi-dimensional time series. More specifically, for each activity video, either complete or incomplete, we first decompose it into a sequence of short video segments. Then, we represent each segment by the local spatial-temporal statistics using the classical bag-of visual -words model. In this way, the comparison between a query sequence (i.e., containing an incomplete activity) and a reference sequence (i.e., containing a full activity) boils down to the problem of aligning their corresponding segment sequences. While GTW treats different portions of a sequence as equally important, our task is in favor of early portions since an incomplete activity video always aligns from the beginning of a complete one. Thus motivated, we develop a temporally-weighted GTW (TGTW) algorithm for the activity prediction problem by encouraging alignment in the early portion of an activity sequence. Finally, the similarity derived from TGTW is combined with the k-nearest neighbors algorithm for predicting the activity class of an input sequence. 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.
WOS关键词ACTION RECOGNITION ; HUMAN MOTION ; ALIGNMENT ; SEQUENCES
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000392164400014
资助机构National Natural Science Foundation of China(61603080 ; Fundamental Research Funds for the Central Universities of China(N150403006) ; NSF of Jiangsu Province(BK20140566 ; China Postdoctoral science Foundation(2014M561586) ; 61473086 ; BK20150470) ; 61375001)
源URL[http://ir.ia.ac.cn/handle/173211/14360]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
作者单位1.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
2.Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA
推荐引用方式
GB/T 7714		 Wang, Haoran,Yang, Wankou,Yuan, Chunfeng,et al. Human activity prediction using temporally-weighted generalized time warping[J]. NEUROCOMPUTING,2017,225(1):139-147.
APA Wang, Haoran,Yang, Wankou,Yuan, Chunfeng,Ling, Haibin,&Hu, Weiming.(2017).Human activity prediction using temporally-weighted generalized time warping.NEUROCOMPUTING,225(1),139-147.
MLA Wang, Haoran,et al."Human activity prediction using temporally-weighted generalized time warping".NEUROCOMPUTING 225.1(2017):139-147.

入库方式： OAI收割

来源：自动化研究所

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