Estimating count and density maps from crowd images has a wide range of applications
such as video surveillance, traffic monitoring, public safety and urban planning. In addition …

When overpopulated cities face frequent crowded events like strikes, demonstrations,
parades or other sorts of people gatherings, they are confronted to multiple security issues …

In crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …

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 …

Recently, counting the number of people for crowd scenes is a hot topic because of its
widespread applications (eg video surveillance, public security). It is a difficult task in the …

State-of-the-art methods for counting people in crowded scenes rely on deep networks to
estimate crowd density. They typically use the same filters over the whole image or over …

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

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 …

In this paper, we propose a novel encoder-decoder network, called extit {Scale Aggregation
Network (SANet)}, for accurate and efficient crowd counting. The encoder extracts multi …

We propose a network for Congested Scene Recognition called CSRNet to provide a data-
driven and deep learning method that can understand highly congested scenes and perform …