Effect of Using Object Shape Prior on Visual Object Counting

Abstract

Visual object counting aims to count the number of objects in a given image or video. Among many object counting methods, the counting by density estimation method draws attention because of its capability of counting and its object localization ability. The method utilizes the object density map of the image with multiple objects. The density is estimated by a regression model which learns the mapping between the local features of the given image and the density map generated from its corresponding object locations. Unlike conventional methods that only rely on object locations for density map generation, in this paper, we show that the system performance can be increased by considering object shapes as well as object locations. To this end, we propose two approaches to generate the ground truth density map from the object locations. Both methods generate the density map which reflects structural features of the objects. We show that the regression models trained with density maps which reflect the object shape outperforms the models trained with density maps generated by the conventional density map generation method on several challenging benchmarks. In other words, we observe that it is essential to generate the ground truth density map according to object shape in the image.