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题名: 基于BSN识别双人交互动作方法的研究
其他题名: Activity recognition of two-body interactions by using BSN
作者: 陈野 ; 王哲龙 ; 武东辉
作者部门: 机器人学研究室
关键词: 体感网 ; 双人交互动作 ; 隐马尔可夫模型 ; 数据融合 ; 一阶逻辑 ; 马尔可夫逻辑网
刊名: 计算机工程与应用
ISSN号: 1002-8331
出版日期: 2014
卷号: 50, 期号:13, 页码:1-5, 20
收录类别: CSCD
产权排序: 2
摘要: 基于体感网对人体动作进行识别的很多研究都是针对单人动作,很少有工作讨论双人交互动作的识别。针对双人交互动作中两人肢体行为的特点,提出了一种隐马尔可夫模型和马尔可夫逻辑网相结合的方法。其中,单人原子行为通过建立隐马尔可夫模型来进行识别,在两人交互行为的语义建模中,建立一阶逻辑知识库,并通过训练马尔可夫逻辑网来最终实现两人交互行为的决策。实验结果表明,与基于特征层数据融合的一些方法相比获得了更高的识别精度,能够有效地识别出双人交互动作。
英文摘要: Existing work in human activity recognition based on body sensor networks (BSN) mainly focuses on recognizing single-user activities and lacks of discussions about two-body interactive activities. A new hierarchical recognition framework which consisted of Hidden Markov Model (HMM) and Markov Logic Network (MLN) was proposed according to the characteristics of two-body interactive actions. The primitive actions of a single person were recognized by using Hidden Markov Model, and the final decision of interactive actions was made by constructing first-order logic knowledge base and employing Markov Logic Network. Experiment results on the interaction dataset have shown that the proposed method could achieve a higher accuracy compared to other methods in activity recognition of two-body interactions.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/14692
Appears in Collections:机器人学研究室_期刊论文

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