Sequentially Supervised Long Short-Term Memory for Gesture Recognition
文献类型：期刊论文
| 作者 | Wang, Peisong ; Song, Qiang; Han, Hua; Cheng, Jian
|
| 刊名 | COGNITIVE COMPUTATION
 |
| 出版日期 | 2016-10-01 |
| 卷号 | 8期号:5页码:982-991 |
| 关键词 | Gesture Recognition Pose Estimation Lstm Sequential Classification |
| DOI | 10.1007/s12559-016-9388-6 |
| 文献子类 | Article |
| 英文摘要 | Gesture recognition has been suffering from long-term dependencies and complex variations in both spatial and temporal dimensions. Many traditional methods use hand cropping and sliding window scheme in the spatial and temporal space, respectively. In this paper, we propose a sequentially supervised long short-term memory architecture, which allows using pose information to guide the learning process of gesture recognition using variable length inputs. Technically, we add supervision at each frame using human joint positions. Our proposed methods can solve gesture recognition and pose estimation problems simultaneously using only RGB videos without hand cropping. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed framework compared with the state-of-the-art methods. |
| WOS研究方向 | Computer Science ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000386351500018 |
| 资助机构 | National Natural Science Foundation of China(61332016) ; Project of Chinese Academy of Sciences(XDB02060001) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13327]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Peisong,Song, Qiang,Han, Hua,et al. Sequentially Supervised Long Short-Term Memory for Gesture Recognition[J]. COGNITIVE COMPUTATION,2016,8(5):982-991. |
| APA | Wang, Peisong,Song, Qiang,Han, Hua,&Cheng, Jian.(2016).Sequentially Supervised Long Short-Term Memory for Gesture Recognition.COGNITIVE COMPUTATION,8(5),982-991. |
| MLA | Wang, Peisong,et al."Sequentially Supervised Long Short-Term Memory for Gesture Recognition".COGNITIVE COMPUTATION 8.5(2016):982-991. |
入库方式： OAI收割
来源：自动化研究所
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