Object counting aims to estimate the number of objects in images. The leading counting
approaches focus on single-category counting tasks and achieve impressive performance …

Crowd counting has recently attracted significant attention in the field of computer vision due
to its wide applications to image understanding. Numerous methods have been proposed …

Weakly supervised crowd counting involves the regression of the number of individuals
present in an image, using only the total number as the label. However, this task is plagued …

Crowd counting is a challenging task due to the heavy occlusions, scales, and density
variations. Existing methods handle these challenges effectively while ignoring low …

Object counting, defined as the task of accurately predicting the number of objects in static
images or videos, has recently attracted considerable interest. However, the unavoidable …

Crowd counting and Crowd density map estimation face several challenges, including
occlusions, non-uniform density, and intra-scene scale and perspective variations …

Crowd counting is a practical yet essential research topic in computer vision, which has
been beneficial to diverse applications in smart city environment safety. The commonly …

Most existing weakly supervised crowd counting methods utilize Convolutional Neural
Networks (CNN) or Transformer to estimate the total number of individuals in an image …

This work considers supervised learning to count from images and their corresponding point
annotations. Where density-based counting methods typically use the point annotations only …

Crowd counting models in highly congested areas confront two main challenges: weak
localization ability and difficulty in differentiating between foreground and background …