基于BSN识别双人交互动作方法的研究
Activity recognition of two-body interactions by using BSN
查看参考文献19篇
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文摘
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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,很少有研究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,该方法获得了更高的识别精度,能够有效地识别出双人交互动作。 |
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其他语种文摘
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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 consists of Hidden Markov Model(HMM)and Markov Logic Network(MLN)is proposed according to the characteristics of two-body interactive actions. The primitive actions of a single person are recognized by using Hidden Markov Model, and the final decision of interactive actions is made by constructing first-order logic knowledge base and employing MLN. Experimental results on the interaction dataset show that the proposed method can achieve a higher accuracy compared to other methods in activity recognition of two-body interactions. |
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来源
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计算机工程与应用
,2014,50(13):1-5,20 【核心库】
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关键词
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体感网
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双人交互动作
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隐马尔可夫模型
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数据融合
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一阶逻辑
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马尔可夫逻辑网
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地址
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1.
大连理工大学控制科学与工程学院, 辽宁, 大连, 116024
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大连理工大学控制科学与工程学院, 机器人学国家重点实验室, 辽宁, 大连, 116024
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1002-8331 |
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学科
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自动化技术、计算机技术 |
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基金
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国家高技术研究发展计划(863)
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国家自然科学基金
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国家科技支撑计划项目
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辽宁省高等学校杰出青年学者成长计划项目
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文献收藏号
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CSCD:5223367
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19
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