Crowd counting is a challenging task due to factors such as large variations in crowdedness
and severe occlusions. Although recent deep learning based counting algorithms have …

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 …

Crowd counting is receiving rapidly growing research interests due to its potential
application value in numerous real-world scenarios. However, due to various challenges …

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 is an important topic in computer vision due to its practical usage in
surveillance systems. The typical design of crowd counting algorithms is divided into two …

Crowd counting is a challenging task in the presence of drastic scale variations, the clutter
background, and severe occlusions, etc. Existing CNN-based counting methods tackle these …

Crowd counting has recently generated huge popularity in computer vision, and is extremely
challenging due to the huge scale variations of objects. In this paper, we propose the …

Crowd counting is a difficult task because of the diversity of scenes. Most of the existing
crowd counting methods adopt complex structures with massive backbones to enhance the …

Most recent methods used for crowd counting are based on the convolutional neural
network (CNN), which has a strong ability to extract local features. But CNN inherently fails …

In this work, we propose a novel crowd counting network that progressively generates crowd
density maps via residual error estimation. The proposed method uses VGG16 as the …