People Count Estimation In Small Crowds

Abstract

This work addresses the problem of people counting in crowded situations, such as urban environments, in computer vision. As crowding density increases in a scene, it might become impossible to count people as single individuals: a global group-based approach is then preferable and in fact often necessary. A simple method for estimating the count of people in such tight crowds is here proposed, relying on accurate camera calibration. A training phase is also needed by the algorithm in order to learn the parameters needed for estimation.