In crowd counting datasets, each person is annotated by a point, which is usually the center
of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of …

With multiple crowd gatherings of millions of people every year in events ranging from
pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd …

Crowd counting has recently attracted increasing interest in computer vision but remains a
challenging problem. In this paper, we propose a trellis encoder-decoder network (TEDnet) …

The accuracy of crowd counting in images has improved greatly in recent years due to the
development of deep neural networks for predicting crowd density maps. However, most …

We address the problem of crowd localization, ie, the prediction of dots corresponding to
people in a crowded scene. Due to various challenges, a localization method is prone to …

Real time crowd analysis represents an active area of research within the computer vision
community in general and scene analysis in particular. Over the last 10 years, various …

Crowd counting is an important topic in computer vision due to its practical usage in
surveillance systems. The typical design of crowd counting algorithms is divided into two …

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 …

To promote the developments of object detection, tracking and counting algorithms in drone-
captured videos, we construct a benchmark with a new drone-captured large-scale dataset …

Crowd counting is a new frontier in computer vision with far-reaching applications
particularly in social safety management. A majority of existing works adopt a methodology …