CAJ | 학술논문

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。
침대인군인수통계중분할특정여문리특정상분리이급회귀모형정도제고적문제，제출일충기우PCA화다원통계회귀상결합적인군인수통계방법。통과PCA대제취도적인군전경분할특정화문리특정진행강유처리；건립다원선성회귀모형，이학정특정량화인군인수지간관계적추세방향；통과회귀출적추세방향，대고사과정회귀모형진행수정。실험결과표명해방법경괄합진행대규모인군인수통계。
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.