刘健. 低维人体运动数据边界智能识别方法研究[J]. 微电子学与计算机, 2018, 35(12): 141-144.
引用本文: 刘健. 低维人体运动数据边界智能识别方法研究[J]. 微电子学与计算机, 2018, 35(12): 141-144.
LIU Jian. Intelligent Recognition Method of Low Dimensional Human Motion Data Boundary[J]. Microelectronics & Computer, 2018, 35(12): 141-144.
Citation: LIU Jian. Intelligent Recognition Method of Low Dimensional Human Motion Data Boundary[J]. Microelectronics & Computer, 2018, 35(12): 141-144.

低维人体运动数据边界智能识别方法研究

Intelligent Recognition Method of Low Dimensional Human Motion Data Boundary

  • 摘要: 提出一种基于径向基函数神经网络的低维人体运动数据边界智能识别方法.采用中值滤波和后验Wiener滤波器对低维人体运动数据边界进行滤波, 以提高边界去噪强度; 利用小波变换对人体运动特征进行分离, 获取分离矩阵以及独立分量, 完成数据特征提取, 获得最适合识别的特征子空间.通过径向基函数神经网络对获取的特征子空间进行识别, 完成低维人体运动数据边界的智能识别.实验表明, 该方法可简化人体运动数据边界特征提取流程, 同时提升人体运动数据边界识别的准确率.

     

    Abstract: An intelligent recognition method based on radial basis function (RBF) neural network for moving data of low dimensional human body is proposed. The median filter and a posteriori Wiener filter are used to filter the boundary of human moving data in order to improve the edge denoising intensity, and the wavelet transform is used to separate the human motion characteristics to obtain the separation matrix and independent components. The data feature extraction is completed to obtain the most suitable feature subspace for recognition. Through radial basis The feature subspace is recognized by the function neural network, and the intelligent recognition of the moving data boundary of the low-dimensional human body is completed. Experiments show that this method can simplify the extraction process of human motion data boundary and improve the accuracy of human motion data boundary recognition.

     

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