In the last decade, crowd counting and localization attract much attention of researchers due
to its wide-spread applications, including crowd monitoring, public safety, space design, etc …

Crowd counting is an effective tool for situational awareness in public places. Automated
crowd counting using images and videos is an interesting yet challenging problem that has …

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 …

Modern crowd counting methods usually employ deep neural networks (DNN) to estimate
crowd counts via density regression. Despite their significant improvements, the regression …

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 …

Imbalanced data distribution in crowd counting datasets leads to severe under-estimation
and over-estimation problems, which has been less investigated in existing works. In this …

Estimating count and density maps from crowd images has a wide range of applications
such as video surveillance, traffic monitoring, public safety and urban planning. In addition …

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 …

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 …

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 …