66,964 research outputs found
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
Drone systems have been deployed by various law enforcement agencies to
monitor hostiles, spy on foreign drug cartels, conduct border control
operations, etc. This paper introduces a real-time drone surveillance system to
identify violent individuals in public areas. The system first uses the Feature
Pyramid Network to detect humans from aerial images. The image region with the
human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network
for human pose estimation. The orientations between the limbs of the estimated
pose are next used to identify the violent individuals. The proposed deep
network can learn meaningful representations quickly using ScatterNet and
structural priors with relatively fewer labeled examples. The system detects
the violent individuals in real-time by processing the drone images in the
cloud. This research also introduces the aerial violent individual dataset used
for training the deep network which hopefully may encourage researchers
interested in using deep learning for aerial surveillance. The pose estimation
and violent individuals identification performance is compared with the
state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV)
workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube
demo at this: https://www.youtube.com/watch?v=zYypJPJipY
Loss Guided Activation for Action Recognition in Still Images
One significant problem of deep-learning based human action recognition is
that it can be easily misled by the presence of irrelevant objects or
backgrounds. Existing methods commonly address this problem by employing
bounding boxes on the target humans as part of the input, in both training and
testing stages. This requirement of bounding boxes as part of the input is
needed to enable the methods to ignore irrelevant contexts and extract only
human features. However, we consider this solution is inefficient, since the
bounding boxes might not be available. Hence, instead of using a person
bounding box as an input, we introduce a human-mask loss to automatically guide
the activations of the feature maps to the target human who is performing the
action, and hence suppress the activations of misleading contexts. We propose a
multi-task deep learning method that jointly predicts the human action class
and human location heatmap. Extensive experiments demonstrate our approach is
more robust compared to the baseline methods under the presence of irrelevant
misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP)
on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\%
relative improvements over the best results reported in the literature, and
thus set new state-of-the-art results. Additionally, unlike some existing
methods, we eliminate the requirement of using a person bounding box as an
input during testing.Comment: Accepted to appear in ACCV 201
PifPaf: Composite Fields for Human Pose Estimation
We propose a new bottom-up method for multi-person 2D human pose estimation
that is particularly well suited for urban mobility such as self-driving cars
and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF)
to localize body parts and a Part Association Field (PAF) to associate body
parts with each other to form full human poses. Our method outperforms previous
methods at low resolution and in crowded, cluttered and occluded scenes thanks
to (i) our new composite field PAF encoding fine-grained information and (ii)
the choice of Laplace loss for regressions which incorporates a notion of
uncertainty. Our architecture is based on a fully convolutional, single-shot,
box-free design. We perform on par with the existing state-of-the-art bottom-up
method on the standard COCO keypoint task and produce state-of-the-art results
on a modified COCO keypoint task for the transportation domain.Comment: CVPR 201
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