Zenithal isotropic object counting by localization using adversarial training
Abstract
Counting objects in images is a very time-consuming task for humans that yields to errors caused by repetitiveness and boredom. In this paper, we present a novel object counting method that, unlike most of the recent works that focus on the regression of a density map, performs the counting procedure by localizing each single object. This key difference allows us to provide not only an accurate count but the position of every counted object,...
Paper Details
Title
Zenithal isotropic object counting by localization using adversarial training
Published Date
Jan 1, 2022
Journal
Volume
145
Pages
155 - 163
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