基于PCA和多元统计回归的人群人数统计方法

摘要

针对人群人数统计中分割特征与纹理特征相分离以及回归模型精度提高的问题，提出一种基于PCA和多元统计回归相结合的人群人数统计方法。通过PCA对提取到的人群前景分割特征和纹理特征进行降维处理；建立多元线性回归模型，以确定特征量和人群人数之间关系的趋势方向；通过回归出的趋势方向，对高斯过程回归模型进行修正。实验结果表明该方法更适合进行大规模人群人数统计。%To solve the problems of the separation of segmentation characteristics and texture features in the crowd statistic, together with improving the accuracy in regression model, this paper proposes a new kind of method of crowd statistic based on PCA and multivariate statistical regression. The research takes the measure of PCA in order to reduce the dimension of the crowd prospect segmentation features and texture features which are extracted. This paper establishes a multiple linear regression model so as to determine the trend of the relationship between characteristic quantity and the number of crowd. The research modifies the Gaussian process regression model according to the trend. The experimental result shows that this method is more suitable for the statistic of large-scale crowd.