Recently, density map regression-based methods have dominated in crowd counting owing
to their excellent fitting ability on density distribution. However, further improvement tends to …

With the growth of urban population, crowd analysis has become an important and
necessary task in the field of computer vision. The goal of crowd counting, which is a …

Tremendous variation in the scale of people/head size is a critical problem for crowd
counting. To improve the scale invariance of feature representation, recent works …

Dense crowd counting remains a challenging task due to complex backgrounds, large-scale
scenes, diversity of human features, and computational performance requirements. Unlike …

While the performance of crowd counting via deep learning has been improved dramatically
in the recent years, it remains an ingrained problem due to cluttered backgrounds and …

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 …

Crowd counting is a very difficult task due to the presence of cluttered backgrounds in crowd
scenes. Although recent counting algorithms have achieved great progress, most of them …

Crowd counting, a significant but challenging task in computer vision, aims at estimating the
number of people in an image or video. Recent methods for crowd counting have obtained …

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 …

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 …