基于改进GA-BP-MC神经网络的人体三维尺寸预测

平面图像由于缺少深度信息从而难以从中获取人体的三维尺寸信息，传统线性回归方法拟合的尺寸信息为该人体所属阈值区间的均值，由于忽视了人体自身的异构性，导致拟合的尺寸误差较大。采用模型重建的方法，可以提高尺寸获取的精度。但是，利用深度神经网络的方法由于计算量与参数规模较大，难以部署到移动终端中。因此，提出了基于改进GA-BP-MC神经网络的人体三维尺寸预测模型UGA-BP-MC，通过改进自适应交叉、变异概率优化的遗传算法（UGA）对BP网络的结构、权值和阈值进行优化，然后采用马尔科夫残差模型对UGA-BP模型输出值进行修正。最后通过工程实例对210组样本进行数据对比分析发现，相对于超椭圆曲线法、多元函数模型和GA-BP模型，UGA-BP-MC预测值的平均误差分别减少了2.8 cm, 1.62 cm和0.94 cm。

3D human dimension prediction based on improved GA-BP-MC neural network

Due to lack of depth information in 2D pictures, it is difficult to obtain the three- dimensional size information of the human body. The size information fitted by the classical linear regression method is the mean value of the threshold interval to which the human body belongs. Because the heterogeneity of the human body is ignored, the size error of the fitting is very large. Model reconstruction can improve the accuracy of size acquisition. However, due to the large scale of computations and parameters in deep neural network, it is difficult to deploy it in mobile devices. Therefore, a 3D human dimension prediction model based on improved GA-BP-MC neural network is proposed. This model optimizes the BP network structure, weights and thresholds by upgrading the adaptive crossover and mutation probability of genetic algorithm. In addition, the Markov residual network is used to improve the prediction accuracy of the UGA-BP model. Finally, data comparison and analysis of 210 sets of samples were carried out through engineering examples, and the results show that, compared with Hyperelliptic curve method, multivariate function and GA-BP model, the proposed UGA-BP-MC reduces the average prediction error by 2.8cm, 1.62cm and 0.94cm respectively.