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.

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