Counting people by clustering person detector outputs

Abstract

We present a people counting system that estimates the number of people in a scene by employing a clustering scheme based on Dirichlet Process Mixture Models (DP-MMs) which takes outputs of a person detector system as input. For each frame, we run a person detector on the frame, take its output as a set of detection areas and define a set of features based on spatial, color and temporal information for each detection. Then using these features, we cluster the detections using DPMMs and Gibbs sampling while having no restriction on the number of clusters, thus can estimate an arbitrary number of people or groups of people. We finally define a measure to calculate the actual number of people within each cluster to infer the final estimation of the number of people in the scene.