A survey of recent advances in cnn-based single image crowd counting and density estimation
VA Sindagi, VM Patel - Pattern Recognition Letters, 2018 - Elsevier
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 …
such as video surveillance, traffic monitoring, public safety and urban planning. In addition …
[HTML][HTML] Recent trends in crowd analysis: A review
When overpopulated cities face frequent crowded events like strikes, demonstrations,
parades or other sorts of people gatherings, they are confronted to multiple security issues …
parades or other sorts of people gatherings, they are confronted to multiple security issues …
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 …
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 …
Transcrowd: weakly-supervised crowd counting with transformers
The mainstream crowd counting methods usually utilize the convolution neural network
(CNN) to regress a density map, requiring point-level annotations. However, annotating …
(CNN) to regress a density map, requiring point-level annotations. However, annotating …
Composition loss for counting, density map estimation and localization in dense crowds
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 …
pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd …
Scale aggregation network for accurate and efficient crowd counting
X Cao, Z Wang, Y Zhao, F Su - Proceedings of the …, 2018 - openaccess.thecvf.com
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 …
Network (SANet)}, for accurate and efficient crowd counting. The encoder extracts multi …
Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes
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 …
driven and deep learning method that can understand highly congested scenes and perform …