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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
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2016-08-01
Volume38Issue:8Pages:1626-1639
SubtypeArticle
AbstractAvailability 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.
KeywordOne-shot Learning Gesture Reco Gnition Rgb-d Data Bag Of Visual Words Model
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2015.2513479
WOS KeywordTIME ACTION RECOGNITION ; BAG
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000379926200011
Citation statistics
Cited Times:43[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12157
Collection模式识别国家重点实验室_生物识别与安全技术研究
Corresponding AuthorJun Wan
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
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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