Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition
文献类型：期刊论文
| 作者 | Wan, Jun1,2 ; Guo, Guodong3; Li, Stan Z.1,2; Jun Wan
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2016-08-01 |
| 卷号 | 38期号:8页码:1626-1639 |
| 关键词 | One-shot Learning Gesture Reco Gnition Rgb-d Data Bag Of Visual Words Model |
| DOI | 10.1109/TPAMI.2015.2513479 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | TIME ACTION RECOGNITION ; BAG |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000379926200011 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12157]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心 |
| 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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