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 mainstream crowd counting methods usually utilize the convolution neural network
(CNN) to regress a density map, requiring point-level annotations. However, annotating …

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 …

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 …

Supervised crowd counting relies heavily on costly manual labeling, which is difficult and
expensive, especially in dense scenes. To alleviate the problem, we propose a novel …

Crowd counting is a fundamental and challenging task that requires rich information to
generate a pixel-level crowd density map. Additionally, the development of thermal sensing …

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 …

Crowd counting is a fundamental problem in crowd analysis which is typically accomplished
by estimating a crowd density map and summing over the density values. However this …

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 …

The infrared and visible image fusion aims to generate one image with rich information by
integrating thermal regions from the infrared image and texture details from the visible …