Convolutional-neural network-based image crowd counting: Review, categorization, analysis, and performance evaluation
Traditional handcrafted crowd-counting techniques in an image are currently transformed
via machine-learning and artificial-intelligence techniques into intelligent crowd-counting …
via machine-learning and artificial-intelligence techniques into intelligent crowd-counting …
Approaches on crowd counting and density estimation: a review
In recent years, urgent needs for counting crowds and vehicles have greatly promoted
research of crowd counting and density estimation. Benefiting from the rapid development of …
research of crowd counting and density estimation. Benefiting from the rapid development of …
Distribution matching for crowd counting
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 …
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …
A generalized loss function for crowd counting and localization
Previous work shows that a better density map representation can improve the performance
of crowd counting. In this paper, we investigate learning the density map representation …
of crowd counting. In this paper, we investigate learning the density map representation …
Bayesian loss for crowd count estimation with point supervision
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 …
of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of …
Learning from synthetic data for crowd counting in the wild
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 …
widespread applications (eg video surveillance, public security). It is a difficult task in the …
Context-aware crowd counting
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 …
estimate crowd density. They typically use the same filters over the whole image or over …
Attention scaling for crowd counting
Abstract Convolutional Neural Network (CNN) based methods generally take crowd
counting as a regression task by outputting crowd densities. They learn the mapping …
counting as a regression task by outputting crowd densities. They learn the mapping …
Crowd counting in the frequency domain
This paper investigates crowd counting in the frequency domain, which is a novel direction
compared to the traditional view in the spatial domain. By transforming the density map into …
compared to the traditional view in the spatial domain. By transforming the density map into …
Crowd counting with deep structured scale integration network
Automatic estimation of the number of people in unconstrained crowded scenes is a
challenging task and one major difficulty stems from the huge scale variation of people. In …
challenging task and one major difficulty stems from the huge scale variation of people. In …