The task of crowd counting is to automatically estimate the pedestrian number in crowd
images. To cope with the scale and perspective changes that commonly exist in crowd …

Crowd counting has important applications in the environments of smart cities, such as
intelligent surveillance. In this paper, we propose a novel convolutional neural network …

Counting objects in digital images is a process that should be replaced by machines. This
tedious task is time consuming and prone to errors due to fatigue of human annotators. The …

Automatic analysis of highly crowded people has attracted extensive attention from
computer vision research. Previous approaches for crowd counting have already achieved …

With multiple crowd gatherings of millions of people every year in events ranging from
pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd …

Crowd counting or density estimation is a challenging task in computer vision due to large
scale variations, perspective distortions and serious occlusions, etc. Existing methods …

Crowd counting is a challenging problem due to the scene complexity and scale variation.
Although deep learning has achieved great improvement in crowd counting, scene …

State-of-the-art methods for counting people in crowded scenes rely on deep networks to
estimate crowd density. While effective, these data-driven approaches rely on large amount …

In this paper we propose a technique to adapt a convolutional neural network (CNN) based
object counter to additional visual domains and object types while still preserving the …

Crowd counting methods typically predict a density map as an intermediate representation
of counting, and achieve good performance. However, due to the perspective phenomenon …