4 research outputs found
Crowd Transformer Network
In this paper, we tackle the problem of Crowd Counting, and present a crowd
density estimation based approach for obtaining the crowd count. Most of the
existing crowd counting approaches rely on local features for estimating the
crowd density map. In this work, we investigate the usefulness of combining
local with non-local features for crowd counting. We use convolution layers for
extracting local features, and a type of self-attention mechanism for
extracting non-local features. We combine the local and the non-local features,
and use it for estimating crowd density map. We conduct experiments on three
publicly available Crowd Counting datasets, and achieve significant improvement
over the previous approaches
Uncertainty Estimation and Sample Selection for Crowd Counting
We present a method for image-based crowd counting, one that can predict a
crowd density map together with the uncertainty values pertaining to the
predicted density map. To obtain prediction uncertainty, we model the crowd
density values using Gaussian distributions and develop a convolutional neural
network architecture to predict these distributions. A key advantage of our
method over existing crowd counting methods is its ability to quantify the
uncertainty of its predictions. We illustrate the benefits of knowing the
prediction uncertainty by developing a method to reduce the human annotation
effort needed to adapt counting networks to a new domain. We present sample
selection strategies which make use of the density and uncertainty of
predictions from the networks trained on one domain to select the informative
images from a target domain of interest to acquire human annotation. We show
that our sample selection strategy drastically reduces the amount of labeled
data from the target domain needed to adapt a counting network trained on a
source domain to the target domain. Empirically, the networks trained on
UCF-QNRF dataset can be adapted to surpass the performance of the previous
state-of-the-art results on NWPU dataset and Shanghaitech dataset using only
17 of the labeled training samples from the target domain.Comment: ACCV 202
Monitoring Social-distance in Wide Areas during Pandemics: a Density Map and Segmentation Approach
With the relaxation of the containment measurements around the globe,
monitoring the social distancing in crowded public places is of grate
importance to prevent a new massive wave of COVID-19 infections. Recent works
in that matter have limited themselves by detecting social distancing in
corridors up to small crowds by detecting each person individually considering
the full body in the image. In this work, we propose a new framework for
monitoring the social-distance using end-to-end Deep Learning, to detect crowds
violating the social-distance in wide areas where important occlusions may be
present. Our framework consists in the creation of a new ground truth based on
the ground truth density maps and the proposal of two different solutions, a
density-map-based and a segmentation-based, to detect the crowds violating the
social-distance constrain. We assess the results of both approaches by using
the generated ground truth from the PET2009 and CityStreet datasets. We show
that our framework performs well at providing the zones where people are not
following the social-distance even when heavily occluded or far away from one
camera.Comment: Video: https://youtu.be/TwzBMKg7h_
Attention, please! A survey of Neural Attention Models in Deep Learning
In humans, Attention is a core property of all perceptual and cognitive
operations. Given our limited ability to process competing sources, attention
mechanisms select, modulate, and focus on the information most relevant to
behavior. For decades, concepts and functions of attention have been studied in
philosophy, psychology, neuroscience, and computing. For the last six years,
this property has been widely explored in deep neural networks. Currently, the
state-of-the-art in Deep Learning is represented by neural attention models in
several application domains. This survey provides a comprehensive overview and
analysis of developments in neural attention models. We systematically reviewed
hundreds of architectures in the area, identifying and discussing those in
which attention has shown a significant impact. We also developed and made
public an automated methodology to facilitate the development of reviews in the
area. By critically analyzing 650 works, we describe the primary uses of
attention in convolutional, recurrent networks and generative models,
identifying common subgroups of uses and applications. Furthermore, we describe
the impact of attention in different application domains and their impact on
neural networks' interpretability. Finally, we list possible trends and
opportunities for further research, hoping that this review will provide a
succinct overview of the main attentional models in the area and guide
researchers in developing future approaches that will drive further
improvements.Comment: 66 pages, 24 figure