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基于微多普勒信号三维形状特征的人体目标动作分类方法

, PP. 16-22

Keywords: 微多普勒,动作分类,支持向量机(SVM)

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Abstract:

从三维形状分析的角度研究基于微多普勒特征的人体目标动作分类。为了从人体运动目标的多普勒频移中获取人体姿态、动作信息,将人体动作多普勒信号经短时傅立叶变换后获得的时间、频率和功率三维信息图形作为研究对象,并分析使用点描述算法获取三维形状特征用于分类的可行性。使用多普勒雷达实测20组人体目标的4个不同动作,采用依赖训练数据的迭代超核函数支持向量机对动作特征学习,应用决策树理论进行分类。研究支持向量机核函数的改进。通过实验证明三维信息的实用性以及分类方法的有效性。

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