In recent years, urgent needs for counting crowds and vehicles have greatly promoted
research of crowd counting and density estimation. Benefiting from the rapid development of …

In this paper, we address the large scale variation problem in crowd counting by taking full
advantage of the multi-scale feature representations in a multi-level network. We implement …

One of appealing approaches to counting dense objects, such as crowd, is density map
estimation. Density maps, however, present ambiguous appearance cues in congested …

Object contour detection is the fundamental and preprocessing step for multimedia
applications such as icon generation, object segmentation, and tracking. The quality of …

Accurately estimating the number of individuals contained in an image is the purpose of the
crowd counting. It has always faced two major difficulties: uneven distribution of crowd …

The existing crowd counting methods usually adopt attention mechanisms to tackle
background noise, or apply multilevel features or multiscale context fusion to tackle scale …

Object counting, which aims to count the accurate number of object instances in images, has
been attracting more and more attention. However, challenges such as large-scale variation …

Crowd counting is a valuable technology for extremely dense scenes in the transportation.
Existing methods generally have higher-order inconsistencies between ground truth density …

Imaging through unstable scattering scenes is a challenging task and the focus of attention.
More computational techniques and optimization methods have been introduced in objects …

Recently, crowd counting via estimating a density map has been widely studied. However, it
still has a variety of issues to overcome, such as large-scale variation of population, complex …