.png){kind=icon}
返回
Sequentially Supervised Long Short-Term Memory for Gesture Recognition
.png){kind=icon}
PRE
AI
DOI：10.1007/s12559-016-9388-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Gesture recognition
Pose estimation
LSTM
Sequential classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.3
论文数:
1.6K
被引数:
3.6K
机构
引用论文
.png){kind=icon}
Demonstration of Pattern Division Multiple Access with Message Passing Algorithm in MMW-RoF Systems在毫米波光纤射频系统中基于消息传递算法的模式分割多址接入演示
PREAI
分享收藏MAP Detection of Probabilistically Shaped Constellations in Optical Fiber Transmissions概率整形星座图在光纤传输中的MAP检测
PREAI
分享收藏分享收藏
查看更多内容