In real-world crowd counting applications, the crowd densities vary greatly in spatial and
temporal domains. A detection based counting method will estimate crowds accurately in …

In real-world crowd counting applications, the crowd densities in an image vary greatly.
When facing density variation, humans tend to locate and count the targets in low-density …

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 …

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 …

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

Cross-scene crowd counting is a challenging task where no laborious data annotation is
required for counting people in new target surveillance crowd scenes unseen in the training …

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 …

We propose a novel crowd counting model that maps a given crowd scene to its density.
Crowd analysis is compounded by myriad of factors like inter-occlusion between people due …

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 a highly challenging problem in computer vision and machine learning.
Most previous methods have focused on consistent density crowds, ie, either a sparse or a …