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

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

State-of-the-art approaches for crowd counting resort to deepneural networks to predict
density maps. However, counting people in congested scenes remains a challenging task …

Growing numbers of crowd density estimation methods have been developed in scene
monitoring, crowd safety and on-site management scheduling. We proposed a method for …

Crowd counting presents enormous challenges in the form of large variation in scales within
images and across the dataset. These issues are further exacerbated in highly congested …

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 challenging due to unconstrained imaging factors, eg, background
clutters, non-uniform distribution of people, large scale and perspective variations. Dealing …

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 …

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 …