Kernel Estimates as General Concept for the Measuring of Pedestrian Density

TL;DR

This paper introduces a flexible kernel-based framework for pedestrian density estimation, improving continuity and trend retention, and demonstrates its effectiveness through experimental parametrization and comparison with existing methods.

Contribution

It presents a general kernel-based approach for pedestrian density measurement, unifying and enhancing existing methods with a focus on parametrization and properties.

Findings

Kernel parametrization improves density smoothness and trend retention.

Conic kernels with specific radii produce desirable density estimates.

The kernel approach can replicate results of Voronoi and inverse distance methods.

Abstract

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.