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 …

Crowd counting is a new frontier in computer vision with far-reaching applications
particularly in social safety management. A majority of existing works adopt a methodology …

Counting dense crowds through computer vision technology has attracted widespread
attention. Most crowd counting datasets use point annotations. In this paper, we formulate …

To fully leverage the data captured from different scenes with different view angles while
reducing the annotation cost, this paper studies a novel crowd counting setting, ie only using …

This paper proposes to handle the practical problem of learning a universal model for crowd
counting across scenes and datasets. We dissect that the crux of this problem is the …

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 …

Recent works on crowd counting mainly leverage Convolutional Neural Networks (CNNs) to
count by regressing density maps, and have achieved great progress. In the density map …

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

One of appealing approaches to counting dense objects, such as crowd, is density map
estimation. Density maps, however, present ambiguous appearance cues in congested …

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 …