In the field of crowd counting, the current mainstream CNNbased regression methods simply
extract the density information of pedestrians without finding the position of each person …

Crowd counting using deep convolutional neural networks (CNN) has achieved
encouraging progress in recent years. Nevertheless, how to efficiently address the problems …

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

Crowd counting, ie, estimating the number of people in a crowded area, has attracted much
interest in the research community. Although many attempts have been reported, crowd …

Crowd counting, ie, estimating the number of people in crowded areas, has attracted much
interest in the research community. Although many attempts have been reported, crowd …

The existing approaches for crowd counting usually estimate a density map with deep
convolutional neural network to obtain the crowd counts. Influenced by the background …

Crowd counting has a wide range of applications such as video surveillance and public
safety. Many existing methods only focus on improving the accuracy of counting but ignore …

Crowd counting is a concerned yet challenging task in computer vision. The difficulty is
particularly pronounced by scale variations in crowd images. Most state-of-art approaches …

The aim of crowd counting is to estimate the number of people in images by leveraging the
annotation of center positions for pedestrians' heads. Promising progresses have been …

Image-based crowd counting has gained significant attention due to its widespread
applications in security and surveillance. Recent advancements in deep learning have led to …