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 …

In recent years, urgent needs for counting crowds and vehicles have greatly promoted
research of crowd counting and density estimation. Benefiting from the rapid development of …

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

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 …

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 …

In this paper, we focus on the crowd localization task, a crucial topic of crowd analysis. Most
regression-based methods utilize convolution neural networks (CNN) to regress a density …

Recently, most handwritten mathematical expression recognition (HMER) methods adopt
the encoder-decoder networks, which directly predict the markup sequences from formula …

Most recent methods used for crowd counting are based on the convolutional neural
network (CNN), which has a strong ability to extract local features. But CNN inherently fails …

Significant progress on the crowd counting problem has been achieved by integrating larger
context into convolutional neural networks (CNNs). This indicates that global scene context …

Multi-view crowd counting has been previously proposed to utilize multi-cameras to extend
the field-of-view of a single camera, capturing more people in the scene, and improve …