Crowd localization, predicting head positions, is a more practical and high-level task than
simply counting. Existing methods employ pseudo-bounding boxes or pre-designed …

The accuracy of crowd counting in images has improved greatly in recent years due to the
development of deep neural networks for predicting crowd density maps. However, most …

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 focus on the crowd localization task, a crucial topic of crowd analysis. Most
regression-based methods utilize convolution neural networks (CNN) to regress a density …

Detection-based methods have been viewed unfavorably in crowd analysis due to their poor
performance in dense crowds. However, we argue that the potential of these methods has …

In real-world crowd counting applications, the crowd densities in an image vary greatly.
When facing density variation, humans tend to locate and count the targets in low-density …

Cell localization in medical image analysis is a challenging task due to the significant
variation in cell shape, size and color. Existing localization methods continue to tackle these …

Crowd localization is a new computer vision task, evolved from crowd counting. Different
from the latter, it provides more precise location information for each instance, not just …

Crowd counting and localization have become increasingly important in computer vision
due to their wide-ranging applications. While point-based strategies have been widely used …

Accurately estimating the number of individuals contained in an image is the purpose of the
crowd counting. It has always faced two major difficulties: uneven distribution of crowd …