Traditional handcrafted crowd-counting techniques in an image are currently transformed
via machine-learning and artificial-intelligence techniques into intelligent crowd-counting …

In recent years, urgent needs for counting crowds and vehicles have greatly promoted
research of crowd counting and density estimation. Benefiting from the rapid development of …

In crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …

Previous work shows that a better density map representation can improve the performance
of crowd counting. In this paper, we investigate learning the density map representation …

In crowd counting datasets, each person is annotated by a point, which is usually the center
of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of …

Recently, counting the number of people for crowd scenes is a hot topic because of its
widespread applications (eg video surveillance, public security). It is a difficult task in the …

State-of-the-art methods for counting people in crowded scenes rely on deep networks to
estimate crowd density. They typically use the same filters over the whole image or over …

Abstract Convolutional Neural Network (CNN) based methods generally take crowd
counting as a regression task by outputting crowd densities. They learn the mapping …

This paper investigates crowd counting in the frequency domain, which is a novel direction
compared to the traditional view in the spatial domain. By transforming the density map into …

Automatic estimation of the number of people in unconstrained crowded scenes is a
challenging task and one major difficulty stems from the huge scale variation of people. In …