Trainable estimators for indirect people counting: A comparative study

Estimating the number of people in a scene is a very relevant issue due to the possibility of using it in a large number of contexts where it is necessary to automatically monitor an area for security/safety reasons, for economic purposes, etc. The large number of people counting approaches available in the literature can be roughly abscribed to two categories: direct approaches and indirect ones. In the first category there are methods that first detect people and then count them; differently, the indirect methods face the counting problem by establishing a relation between some scene features and the estimated number of people. Some recent comparative evaluations carried out in the framework of the PETS initiative have demonstrated that the indirect methods tends to be more robust than direct ones, above all when they are used in very crowded conditions. In this paper, we analyze the behavior of an indirect approach that is based on a trainable estimator that does not require an explicit formulation of a priori knowledge about the perspective and density effects present in the scene at hand. In particular, we investigate on the way the counting accuracy in different crowding conditions is affected by the choice of the trainable estimator.