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 …

Localizing individuals in crowds is more in accordance with the practical demands of
subsequent high-level crowd analysis tasks than simply counting. However, existing …

Previous work generally believes that improving the spatial invariance of convolutional
networks is the key to object counting. However, after verifying several mainstream counting …

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 …

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 …

Counting high‐density objects quickly and accurately is a popular area of research. Crowd
counting has significant social and economic value and is a major focus in artificial …

Scale variation is a deep-rooted problem in object counting, which has not been effectively
addressed by existing scale-aware algorithms. An important factor is that they typically …

In this paper, we address the large scale variation problem in crowd counting by taking full
advantage of the multi-scale feature representations in a multi-level network. We implement …

Most recent methods used for crowd counting are based on the convolutional neural
network (CNN), which has a strong ability to extract local features. But CNN inherently fails …

Crowd counting has attracted increasing attentions in recent years due to its challenges and
wide societal applications. Despite persevering efforts made by the research community …