基于模糊神经网络的大场景人群密度估计方法 *

提出了一种估计大场景下密集人群密度的方法。该方法根据人类视觉的模糊性原理，认为用模糊集来划分人群密度范围比用确定的方法更符合人眼视知觉的认知方式，利用统计的方法确定灰度共生矩阵各指标对于各个密度类别的隶属函数；设计基于误差反向传播训练算法（BP）的模糊神经网络，计算样本模式对于各个密度类别的隶属度，并根据人群密度变化的时域连续性原理对人群密度范围进行合理估计。实验表明该方法提高了估计精度。

Crowd density estimation of wide scene based on fuzzy neural network

This paper proposed a crowd density estimation method based on a fuzzy neural network. According to the fuzzy phenomena used by human vision, it was more accordant with cognitive style of human vision by using fuzzy sets to describe crowd density range than other definite measures. Defined functions of membership degree of indicators of the grey level dependence matrix (GLDM) by statistical method and designed a fuzzy neural network based on the error back propagation (BP) training algorithm to calculate the membership degree of the input pattern, which could be explained reasonably according to the temporal continuity of variety of the crowd density. Experimental results show this method performs well and improves the accuracy of estimation.