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 …

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 …

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

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 …

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 …

Crowd counting and crowd density estimation methods are of great significance in the field
of public security. Estimating crowd density and counting from single image or video frame …

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 …

Crowd counting has attracted increasing attentions in recent years due to its challenges and
wide societal applications. Despite persevering efforts made by the research community …

Most existing crowd counting systems rely on the availability of the object location
annotation which can be expensive to obtain. To reduce the annotation cost, one attractive …

Street Scene Change Detection (SSCD) aims to locate the changed regions between a
given street-view image pair captured at different times, which is an important yet …