Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition
Wan, Jun1,2; Guo, Guodong3; Li, Stan Z.1,2; Jun Wan
2016-08-01
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷号38期号:8页码:1626-1639
文章类型Article
摘要Availability of handy RGB-D sensors has brought about a surge of gesture recognition research and applications. Among various approaches, one shot learning approach is advantageous because it requires minimum amount of data. Here, we provide a thorough review about one-shot learning gesture recognition from RGB-D data and propose a novel spatiotemporal feature extracted from RGB-D data, namely mixed features around sparse keypoints (MFSK). In the review, we analyze the challenges that we are facing, and point out some future research directions which may enlighten researchers in this field. The proposed MFSK feature is robust and invariant to scale, rotation and partial occlusions. To alleviate the insufficiency of one shot training samples, we augment the training samples by artificially synthesizing versions of various temporal scales, which is beneficial for coping with gestures performed at varying speed. We evaluate the proposed method on the Chalearn gesture dataset (CGD). The results show that our approach outperforms all currently published approaches on the challenging data of CGD, such as translated, scaled and occluded subsets. When applied to the RGB-D datasets that are not one-shot (e.g., the Cornell Activity Dataset-60 and MSR Daily Activity 3D dataset), the proposed feature also produces very promising results under leave-one-out cross validation or one-shot learning.
关键词One-shot Learning Gesture Reco Gnition Rgb-d Data Bag Of Visual Words Model
WOS标题词Science & Technology ; Technology
DOI10.1109/TPAMI.2015.2513479
关键词[WOS]TIME ACTION RECOGNITION ; BAG
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000379926200011
引用统计
被引频次:31[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12157
专题模式识别国家重点实验室_生物识别与安全技术研究
通讯作者Jun Wan
作者单位1.Chinese Acad Sci, Ctr Biometr & Secur Res, Room 1411,Intelligent Bldg,95 Zhongguancun Donglu, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Room 1411,Intelligent Bldg,95 Zhongguancun Donglu, Beijing 100190, Peoples R China
3.West Virginia Univ, Dept Comp Sci & Elect Engn, Morgantown, WV 26506 USA
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Wan, Jun,Guo, Guodong,Li, Stan Z.,et al. Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2016,38(8):1626-1639.
APA Wan, Jun,Guo, Guodong,Li, Stan Z.,&Jun Wan.(2016).Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,38(8),1626-1639.
MLA Wan, Jun,et al."Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 38.8(2016):1626-1639.
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