Previous work generally believes that improving the spatial invariance of convolutional
networks is the key to object counting. However, after verifying several mainstream counting …

Visual counting, a task that predicts the number of objects from an image/video, is an open-
set problem by nature, ie, the number of population can vary in [0,+[?]) in theory. However …

This paper aims to count arbitrary objects in images. The leading counting approaches start
from point annotations per object from which they construct density maps. Then, their …

The counting problem aims to estimate the number of objects in images. Due to large scale
variation and labeling deviations, it remains a challenging task. The static density map …

Object counting, whose aim is to estimate the number of objects from a given image, is an
important and challenging computation task. Significant efforts have been devoted to …

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 …

Growing numbers of crowd density estimation methods have been developed in scene
monitoring, crowd safety and on-site management scheduling. We proposed a method for …

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 …

Most existing CNN-based methods for crowd counting always suffer from large scale
variation in objects of interest, leading to density maps of low quality. In this paper, we …

In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional
neural network (CNN) based crowd counting (ie PGCNet), which aims to overcome the …