Temporal segmentation and recognition of body motion data based on inter-limb correlation analysis

T. Nakata
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引用次数: 11

Abstract

A method for segmentation and recognition of human body behavior data is proposed. Recognition of human body movements is getting larger interests in robotic research field, since robots must recognize human behavior in order to interact with human in the real world. In addition, there is demand for quantitative methods to analyze human body movements, since human body movements can be used as models of robot behaviors. The author proposes a scheme for human behavior recognition based on two process steps: analysis of movement correlations among limbs and temporal segmentation of motion data. Inter-limb movement correlations are widely observed in various behaviors and well represent contents of behavior, so it will be a universal feature value for general behavior. Observing changes of inter-limb correlations, we can segment motion capture data into temporal fragment of action units. Using this segmentation technique in an experiment, the system succeeded recognizing various types of human behavior efficiently.
基于肢体间相关分析的肢体运动数据时间分割与识别
提出了一种人体行为数据的分割与识别方法。人体运动识别是机器人研究的热点,因为机器人必须识别人类的行为才能与人类在现实世界中进行互动。此外,由于人体运动可以作为机器人行为的模型,因此需要对人体运动进行定量分析的方法。作者提出了一种基于肢体运动关联分析和运动数据时间分割两个步骤的人体行为识别方案。肢体间运动相关性在各种行为中被广泛观察到,并且很好地代表了行为的内容,因此它将成为一般行为的通用特征值。通过观察肢体间相关性的变化,我们可以将动作捕捉数据分割成动作单元的时间片段。在实验中,该系统成功地识别了不同类型的人类行为。
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