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

Supervised crowd counting relies heavily on costly manual labeling, which is difficult and
expensive, especially in dense scenes. To alleviate the problem, we propose a novel …

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 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 …

Counting high‐density objects quickly and accurately is a popular area of research. Crowd
counting has significant social and economic value and is a major focus in artificial …

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 …

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

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 …

Leaf chlorophyll content (LCC) is a distinct indicator of crop health status used to estimate
nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the …

Recent works on crowd counting mainly leverage Convolutional Neural Networks (CNNs) to
count by regressing density maps, and have achieved great progress. In the density map …