Key feature extraction for probabilistic categorization of human motion patterns
Key feature extraction for probabilistic categorization of human motion patterns
复制标题
DOI：
10.1109/icar.2005.1507445
复制
发表时间：
2005-07
期刊：
影响因子：
--
通讯作者：
W. Takano;H. Tanie;Y. Nakamura
中科院分区：
文献类型：
--
作者：
W. Takano;H. Tanie;Y. Nakamura
模仿是一种假设，人类智能起源于运动识别和运动生成通过模仿相互作用。我们以前提出了模仿的数学模型，使用隐马尔可夫模型（HMM）和构造的原始符号空间，从每个HMM的参数。原始符号空间仅包括10种运动模式。对行为模式与身体部位的关系研究较少。人类观察者通常关注身体各部分与识别表演者行为模式的行为之间的关系。在本文中，我们讨论了关键特征提取的基础上，从一个丰富的数据库中的行为模式的概率分类之间的障碍。该方法也适用于提取身体部位的特征行为模式
Mimesis is a hypothesis that human intelligence originated where motion recognition and motion generation interact through imitation. We previously proposed the mathematical model of mimesis using hidden Markov models (HMM) and constructed the proto symbol space from parameters of each HMM. The proto symbol space included only 10 motion patterns. No attention was paid on the relationship between behavior pattern and parts of body. It is common that a human observer pays an attention to the relationship between the parts of body and the behaviors recognizing performer's behavior pattern. In this paper, we discuss key feature extraction from a rich database of behavior patterns based on probabilistic categorization among HMMs. The method is also applied to extract body parts that characterize behavior patterns