The standard definition of pedestrian density produces scattered values, hence, many approaches have been developed to improve the features of the estimated density. This paper provides a review of generally applied methods and presents a general framework based on various kernels that bring desired properties of density estimates (e.g., continuity) and incorporate ordinarily used methods. The developed kernel concept considers each pedestrian as a source of density distribution, parametrized by the kernel type (e.g., Gauss, cone) and kernel size. The quantitative parametric study performed on experimental data illustrates that parametrization brings desired features, for instance, a conic kernel with a base radius in (0.7, 1.2) m produces smooth values that retain trend features. The correspondence between kernel and non-kernel methods (namely Voronoi diagram and customized inverse distance to the nearest pedestrian) is achievable for a wide range of kernel parameter. Thereby the generality of the concept is supported.

翻译：标准定义的行人密度会产生离散值，因此许多方法已被开发用于改进估计密度的特征。本文回顾了通常应用的方法，并提出了一种基于各种核的通用框架，该框架能带来密度估计的理想属性（例如连续性），并整合了常规使用的方法。所提出的核概念将每位行人视为密度分布的源，该分布由核类型（如高斯、圆锥）和核大小参数化。在实验数据上进行的定量参数化研究表明，参数化能带来理想的特征，例如，基半径在（0.7, 1.2）米范围内的圆锥核会产生平滑的数值，同时保留趋势特征。在一定范围的核参数下，核方法与非核方法（即Voronoi图和自定义到最近行人的反距离）之间的对应关系是可实现的，从而支持了该概念的普适性。