Inter-Homines: Distance-Based Risk Estimation for Human Safety
- URL: http://arxiv.org/abs/2007.10243v1
- Date: Mon, 20 Jul 2020 16:32:27 GMT
- Title: Inter-Homines: Distance-Based Risk Estimation for Human Safety
- Authors: Matteo Fabbri, Fabio Lanzi, Riccardo Gasparini, Simone Calderara,
Lorenzo Baraldi, Rita Cucchiara
- Abstract summary: Our system evaluates in real-time the contagion risk in a monitored area by analyzing video streams.
It is able to locate people in 3D space, calculate distances and predict risk levels.
Inter-Ho-mines works both indoor and outdoor, in public and private crowded areas.
- Score: 44.266630835933434
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this document, we report our proposal for modeling the risk of possible
contagiousity in a given area monitored by RGB cameras where people freely move
and interact. Our system, called Inter-Homines, evaluates in real-time the
contagion risk in a monitored area by analyzing video streams: it is able to
locate people in 3D space, calculate interpersonal distances and predict risk
levels by building dynamic maps of the monitored area. Inter-Homines works both
indoor and outdoor, in public and private crowded areas. The software is
applicable to already installed cameras or low-cost cameras on industrial PCs,
equipped with an additional embedded edge-AI system for temporary measurements.
From the AI-side, we exploit a robust pipeline for real-time people detection
and localization in the ground plane by homographic transformation based on
state-of-the-art computer vision algorithms; it is a combination of a people
detector and a pose estimator. From the risk modeling side, we propose a
parametric model for a spatio-temporal dynamic risk estimation, that, validated
by epidemiologists, could be useful for safety monitoring the acceptance of
social distancing prevention measures by predicting the risk level of the
scene.
Related papers
- Monocular 2D Camera-based Proximity Monitoring for Human-Machine
Collision Warning on Construction Sites [1.7223564681760168]
Accident of struck-by machines is one of the leading causes of casualties on construction sites.
Monitoring workers' proximities to avoid human-machine collisions has aroused great concern in construction safety management.
This study proposes a novel framework for proximity monitoring using only an ordinary 2D camera to realize real-time human-machine collision warning.
arXiv Detail & Related papers (2023-05-29T07:47:27Z) - Intersection Warning System for Occlusion Risks using Relational Local
Dynamic Maps [0.0]
This work addresses the task of risk evaluation in traffic scenarios with limited observability due to restricted sensorial coverage.
To identify the area of sight, we employ ray casting on a local dynamic map providing geometrical information and road infrastructure.
Resulting risk indicators are utilized to evaluate the driver's current behavior, to warn the driver in critical situations, to give suggestions on how to act safely or to plan safe trajectories.
arXiv Detail & Related papers (2023-03-13T16:01:55Z) - Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from
Depth Maps [66.24554680709417]
Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications.
We propose a non-invasive framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera.
arXiv Detail & Related papers (2022-07-06T08:52:12Z) - BEV-Net: Assessing Social Distancing Compliance by Joint People
Localization and Geometric Reasoning [77.08836528980248]
Social distancing, an essential public health measure, has gained significant attention since the outbreak of the COVID-19 pandemic.
In this work, the problem of visual social distancing compliance assessment in busy public areas with wide field-of-view cameras is considered.
A dataset of crowd scenes with people annotations under a bird's eye view (BEV) and ground truth for metric distances is introduced.
A multi-branch network, BEV-Net, is proposed to localize individuals in world coordinates and identify high-risk regions where social distancing is violated.
arXiv Detail & Related papers (2021-10-10T23:56:37Z) - Single Image Human Proxemics Estimation for Visual Social Distancing [37.84559773949066]
We propose a semi-automatic solution to approximate the homography matrix between the scene ground and image plane.
We then leverage an off-the-shelf pose detector to detect body poses on the image and to reason upon their inter-personal distances.
arXiv Detail & Related papers (2020-11-03T21:49:13Z) - Risk-Sensitive Sequential Action Control with Multi-Modal Human
Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure.
Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control.
A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
arXiv Detail & Related papers (2020-09-12T02:02:52Z) - Perceiving Humans: from Monocular 3D Localization to Social Distancing [93.03056743850141]
We present a new cost-effective vision-based method that perceives humans' locations in 3D and their body orientation from a single image.
We show that it is possible to rethink the concept of "social distancing" as a form of social interaction in contrast to a simple location-based rule.
arXiv Detail & Related papers (2020-09-01T10:12:30Z) - DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment
in COVID-19 Pandemic [1.027974860479791]
Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places.
We develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network model for automated people detection in the crowd using common CCTV cameras.
The developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields.
arXiv Detail & Related papers (2020-08-26T16:56:57Z) - Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for
Online Collision Avoidance [95.86944752753564]
We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties.
Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates.
The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
arXiv Detail & Related papers (2020-07-28T07:34:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.