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 …

Localizing individuals in crowds is more in accordance with the practical demands of
subsequent high-level crowd analysis tasks than simply counting. However, existing …

In crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …

Accurately estimating the number of objects in a single image is a challenging yet
meaningful task and has been applied in many applications such as urban planning and …

The crowd counting task aims at estimating the number of people located in an image or a
frame from videos. Existing methods widely adopt density maps as the training targets to …

One of the main and most effective measures to contain the recent viral outbreak is the
maintenance of the so-called Social Distancing (SD). To comply with this constraint …

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 …

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

Gathering a large number of people in a shared physical area is very common in urban
culture. Although there are limitless examples of mega crowds, the Islamic religious ritual …

Most existing crowd counting methods require object location-level annotation which is labor-
intensive and time-consuming to obtain. In contrast, weaker annotations that only label the …