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 …

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 …

Accurately estimating the number of objects in a single image is a challenging yet
meaningful task and has been applied in many applications such as urban planning and …

Crowd counting and crowd density estimation methods are of great significance in the field
of public security. Estimating crowd density and counting from single image or video frame …

Most existing crowd counting methods require object location-level annotation which is labor-
intensive and time-consuming to obtain. In contrast, weaker annotations that only label the …

In this paper, we present a novel method Coarse-and Fine-grained Attention Network
(CFANet) for generating high-quality crowd density maps and people count estimation by …

To tackle the negative effect of the arbitrary crowd distribution on the counting task, in this
article, we propose a novel RGB-D crowd counting approach, including a cross-modal cycle …

In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to
address the video-based crowd counting problem, which contains the decomposition of 3D …

To learn a reliable people counter from crowd images, head center annotations are normally
required. Annotating head centers is however a laborious and tedious process in dense …

In this article, we propose a new cross locality relation network (CLRNet) to generate high-
quality crowd density maps for crowd counting in videos. Specifically, a cross locality relation …