Although the traits emerged in a mass gathering are often non-deliberative, the act of mass
impulse may lead to irrevocable crowd disasters. The two-fold increase of carnage in crowd …

We propose to leverage multiple sources of information to compute an estimate of the
number of individuals present in an extremely dense crowd visible in a single image. Due to …

Building upon state-of-the-art algorithms for pedestrian detection and multi-object tracking,
and inspired by sociological models of human collective behavior, we automatically detect …

I noticed a recently published paper named 'Is acupuncture effective in the treatment of
COVID-19 related symptoms? Based on bioinformatics/network topology strategy'with great …

Nearly all existing counting methods are designed for a specific object class. Our work,
however, aims to create a counting model able to count any class of object. To achieve this …

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels
can be detected using holistic image features, however this requires a large amount of …

Crowd management is a flourishing, active research area and must be given attention due to
the potential losses, disasters, and accidents that could occur if it were neglected. For the …

Crowd understanding has drawn increasing attention from the computer vision community,
and its progress is driven by the availability of public crowd datasets. In this paper, we …

In this paper, we propose a fast fully convolutional neural network (FCNN) for crowd
segmentation. By replacing the fully connected layers in CNN with 1 by 1 convolution …

In this paper, we propose a novel human crowd detection method that uses deep
convolutional neural networks for drone flight safety purposes. The first contribution of this …