Adaptive 3D shape context representation for motion trajectory classification | |
Liu, Weihua1; Li, Zuhe2; Zhang, Geng1; Zhang, Zhong3 | |
刊名 | multimedia tools and applications |
2017-07-01 | |
卷号 | 76期号:14页码:15413-15434 |
关键词 | Gesture trajectory Shape context Dynamic time warping Gesture Classification |
ISSN号 | 1380-7501 |
通讯作者 | liu, wh (reprint author), chinese acad sci, xian inst opt & precis mech, key lab spectral imaging technol, xian, peoples r china. |
产权排序 | 1 |
英文摘要 | the measurement of similarity between two motion trajectories is one of the fundamental task for motion analysis, perception and recognition. previous research focus on 2d trajectory similarity measurement. with the advent of 3d sensors, it is possible to collect large amounts of 3d trajectory data for more precise motion representation. as trajectories in 3d space may often exhibit a similar motion pattern but may differ in location, orientation, scale, and appearance variations, the trajectory descriptor must be invariant to these degrees of freedom. shape context is one of the rich local shape descriptors can be used to represent the trajectory in 2d space, however, rarely applied in the 3d motion trajectory recognition field. to handle 3d data, in this paper, we first naturally extend the shape context into the spatiotemporal domain by adopting a spherical neighborhood, and named it 3d shape context(3dsc). to achieve better global invariant on trajectories classification, the adaptive outer radius of 3dsc for extracting 3d shape context feature is proposed. the advantages of our proposed 3d shape context are: (1) it is invariant to motion trajectories translation and scale in the spatiotemporal domain; (2) it contains the whole trajectory points in the 3dsc ball volume, thus can achieve global information representation and is good for solving sub-trajectories problem; (3) it is insensitive to the appearance variations in the identical meaning trajectories, meanwhile, can greatly discriminate the distinct meaning trajectories. in trajectory recognition phase, we consider a feature-to-feature alignment between motion trajectories based on dynamic time warping and then use the one nearest neighbor (1nn) classifier for final accuracy evaluation. we test the performance of proposed 3d sc-dtw on uci asl large dataset, digital hand dataset and the experimental results demonstrate the effectiveness of our method. |
学科主题 | computer science, information systems ; computer science, software engineering ; computer science, theory & methods ; engineering, electrical & electronic |
WOS标题词 | science & technology ; technology |
类目[WOS] | computer science, information systems ; computer science, software engineering ; computer science, theory & methods ; engineering, electrical & electronic |
研究领域[WOS] | computer science ; engineering |
关键词[WOS] | recognition ; retrieval ; gestures |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000404609900010 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/29101] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian, Peoples R China 2.Zhengzhou Univ Light Ind, Sch Comp & Commun Engn, Zhengzhou, Peoples R China 3.Univ Texas Arlington, Arlington, TX 76019 USA |
推荐引用方式 GB/T 7714 | Liu, Weihua,Li, Zuhe,Zhang, Geng,et al. Adaptive 3D shape context representation for motion trajectory classification[J]. multimedia tools and applications,2017,76(14):15413-15434. |
APA | Liu, Weihua,Li, Zuhe,Zhang, Geng,&Zhang, Zhong.(2017).Adaptive 3D shape context representation for motion trajectory classification.multimedia tools and applications,76(14),15413-15434. |
MLA | Liu, Weihua,et al."Adaptive 3D shape context representation for motion trajectory classification".multimedia tools and applications 76.14(2017):15413-15434. |
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