.png){kind=icon}
返回
Sensor fusion based manipulative action recognition
.png){kind=icon}
PRE
AI
DOI：10.1007/s10514-020-09943-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Manipulative action recognition is one of the most important and challenging topic in the fields of image processing. In this paper, three kinds of sensor modules are used for motion, force and object information capture in the manipulative actions. Two fusion methods are proposed. Further, the recognition accuracy can be improved by using object as context. For the feature-level fusion method, significant features are chosen first. Then the Hidden Markov Models are built with these selected features to characterize the temporal sequence. For the decision-level fusion method, HMMs are built for each feature group. Then the decisions are fused. On top of these two fusion methods, the object/action context is modeled using Bayesian network. Assembly tasks are used for algorithm evaluation. The experimental results prove that the proposed approach is effective on manipulative action recognition task. The recognition accuracy of the decision-level, feature-level fusion methods and the Bayesian model are 72%, 80% and 90% respectively.
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:
1.7K
被引数:
5.0K
机构
引用论文
.png){kind=icon}
分享收藏Human papillomavirus genotype profiles and cytological grades interlinkages in coinfection with HIV
OAAI
OAAI分享收藏
A survey of depth and inertial sensor fusion for human action recognition用于人体动作识别的深度和惯性传感器融合研究综述
PREAI
分享收藏
查看更多内容