Abstract：Different human activity classification based on 3 dimension shape of micro-Doppler signatures is studied. The 3D shape information, including time, frequency and power, is achieved by short-time Fourier transform to Doppler to obtain human pose and motion. The algorithm of point description image is used for obtaining the 3D shape characteristic. The data of 4 motions of 20 people are measured by Doppler radar. The action characteristics are learned by SVM with conformal transforming hyperkernel functions. Then the decision tree model is used for action classification. The improved of kernel functions is studied. The proposed human activity classification and the improved kernel functions are validated by experiments.
陈亦望，张品，傅强. 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[J]. 模式识别与人工智能, 2012, 25(1): 16-22.
CHEN Yi-Wang, ZHANG Pin, FU Qiang. Human Activity Classification Based on Features of 3D Micro-Doppler Signatures Shape. , 2012, 25(1): 16-22.
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