.png){kind=icon}
返回
A structured multi-feature representation for recognizing human action and interaction
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2018.08.066.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Active research has been carried out for human action recognition using 3D human skeleton joints with the release of cost-efficient RGB-D sensors. However, extracting discriminative features from noisy skeleton sequences to effectively distinguish various human action or interaction categories still remains challenging. This paper proposes a structured multi-feature representation for human action and interaction recognition. Specifically, a novel kernel enhanced bag of semantic words (BSW) is designed to represent the dynamic property of skeleton trajectories. By aggregating BSW with the geometric feature, a GBSW representation is constructed for human action recognition. For human interaction recognition where the cooperation of each subject matters, a GBSWC representation is proposed via combining the GBSW feature with a correlation feature which addresses the intrinsic relationship between interactive persons. Experimental results on several human action and interaction datasets demonstrate the superior performances of the proposed features over the state-of-the-art methods. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Action recognition
Interaction recognition
RGB-D sensors
Skeleton joints
Multi-feature
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Depth Context: a new descriptor for human activity recognition by using sole depth sequences.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
Enhanced skeleton visualization for view invariant human action recognition用于视图不变人体动作识别的增强骨架可视化PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容