Crowd counting is an effective tool for situational awareness in public places. Automated
crowd counting using images and videos is an interesting yet challenging problem that has …

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

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 …

The mainstream crowd counting methods usually utilize the convolution neural network
(CNN) to regress a density map, requiring point-level annotations. However, annotating …

Abstract Convolutional Neural Network (CNN) based methods generally take crowd
counting as a regression task by outputting crowd densities. They learn the map** …

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 …

Existing works on visual counting primarily focus on one specific category at a time, such as
people, animals, and cells. In this paper, we are interested in counting everything, that is to …

Crowd counting is a fundamental yet challenging task, which desires rich information to
generate pixel-wise crowd density maps. However, most previous methods only used the …

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 …

Semi-supervised approaches for crowd counting attract attention, as the fully supervised
paradigm is expensive and laborious due to its request for a large number of images of …