Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

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 …

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 …

Localizing individuals in crowds is more in accordance with the practical demands of
subsequent high-level crowd analysis tasks than simply counting. However, existing …

We propose a novel crowd counting model that maps a given crowd scene to its density.
Crowd analysis is compounded by myriad of factors like inter-occlusion between people due …

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 …

This paper focuses on crowd counting. As large-scale variations often exist within crowd
images, neither fixed-size convolution kernel of CNN nor fixed-size attentions of recent …

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 …

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 …

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 …