Bayesian Linear Regression for Crowd Density Estimation in Aerial Images

Abstract

In this paper, we propose a Bayesian linear regression method for person density estimation in extremely crowded areas in aerial images. The fundamental idea is to learn a mapping function from local features to crowd density. In order to describe the appearances of persons within a crowd in aerial images, local texture features are computed for each small local neighborhood. Then we cast the problem as a linear regression. In order to model the nonlinearity between local features and crowd density, Gaussian basis functions are used and their locations are determined by a $k$-means clustering. Crowd density can be estimated by Bayesian inference. However, due to the presence of a hyper-prior distribution, variational inference is applied to compute the predictive distribution. Through experiments, the effectiveness of the proposed method for crowd density estimation is demonstrated.