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 …

Counting dense crowds through computer vision technology has attracted widespread
attention. Most crowd counting datasets use point annotations. In this paper, we formulate …

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 …

Crowd counting has important applications in the environments of smart cities, such as
intelligent surveillance. In this paper, we propose a novel convolutional neural network …

Dense crowd counting aims to predict thousands of human instances from an image, by
calculating integrals of a density map over image pixels. Existing approaches mainly suffer …

This work studies the problem of few-shot object counting, which counts the number of
exemplar objects (ie, described by one or several support images) occurring in the query …

In this paper, we consider the problem of crowd counting in images. Given an image of a
crowded scene, our goal is to estimate the density map of this image, where each pixel …

Crowd counting has recently generated huge popularity in computer vision, and is extremely
challenging due to the huge scale variations of objects. In this paper, we propose the …

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 …

Nonlinear regression has been extensively employed in many computer vision problems
(eg, crowd counting, age estimation, affective computing). Under the umbrella of deep …