Knowledge Commons of Institute of Automation,CAS
Human activity prediction using temporally-weighted generalized time warping | |
Wang, Haoran1; Yang, Wankou2; Yuan, Chunfeng3; Ling, Haibin4; Hu, Weiming3 | |
发表期刊 | NEUROCOMPUTING |
2017-02-15 | |
卷号 | 225期号:1页码:139-147 |
文章类型 | Article |
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. |
关键词 | Activity Prediction Time Warping Alignment |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1016/j.neucom.2016.11.004 |
关键词[WOS] | ACTION RECOGNITION ; HUMAN MOTION ; ALIGNMENT ; SEQUENCES |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | 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) |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000392164400014 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/14360 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
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. |
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