15 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
Temporally Consistent Horizon Lines
The horizon line is an important geometric feature for many image processing
and scene understanding tasks in computer vision. For instance, in navigation
of autonomous vehicles or driver assistance, it can be used to improve 3D
reconstruction as well as for semantic interpretation of dynamic environments.
While both algorithms and datasets exist for single images, the problem of
horizon line estimation from video sequences has not gained attention. In this
paper, we show how convolutional neural networks are able to utilise the
temporal consistency imposed by video sequences in order to increase the
accuracy and reduce the variance of horizon line estimates. A novel CNN
architecture with an improved residual convolutional LSTM is presented for
temporally consistent horizon line estimation. We propose an adaptive loss
function that ensures stable training as well as accurate results. Furthermore,
we introduce an extension of the KITTI dataset which contains precise horizon
line labels for 43699 images across 72 video sequences. A comprehensive
evaluation shows that the proposed approach consistently achieves superior
performance compared with existing methods