IoT to monitor people flow in areas of public interest
- URL: http://arxiv.org/abs/2111.04465v1
- Date: Wed, 3 Nov 2021 01:53:49 GMT
- Title: IoT to monitor people flow in areas of public interest
- Authors: Damiano Perri, Marco Simonetti, Alex Bordini, Simone Cimarelli,
Osvaldo Gervasi
- Abstract summary: The aim of this research is to set up a system to monitor the flow of people inside public places and facilities of interest without collecting personal or sensitive data.
Our study, which began as an experiment in the Umbria region of Italy, aims to be one of several answers to automated planning of people's flows in order to make our land more liveable.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The unexpected historical period we are living has abruptly pushed us to
loosen any sort of interaction between individuals, gradually forcing us to
deal with new ways to allow compliance with safety distances; indeed the
present situation has demonstrated more than ever how critical it is to be able
to properly organize our travel plans, put people in safe conditions, and avoid
harmful circumstances. The aim of this research is to set up a system to
monitor the flow of people inside public places and facilities of interest
(museums, theatres, cinemas, etc.) without collecting personal or sensitive
data. Weak monitoring of people flows (i.e. monitoring without personal
identification of the monitored subjects) through Internet of Things tools
might be a viable solution to minimize lineups and overcrowding. Our study,
which began as an experiment in the Umbria region of Italy, aims to be one of
several answers to automated planning of people's flows in order to make our
land more liveable. We intend to show that the Internet of Things gives almost
unlimited tools and possibilities, from developing a basic information process
to implementing a true portal which enables business people to connect with
interested consumers.
Related papers
- Identification of crowds using mobile crowd detection (MCS) and visualization with the DBSCAN algorithm for a Smart Campus environment [0.0]
This paper explores the feasibility of using Mobile Crowd Sensing (MCS) and visualization algorithms to detect crowding on a university campus.
Preliminary results suggest that the system is viable and could be a useful tool for the prevention of accidents due to crowding and for the management of public spaces.
arXiv Detail & Related papers (2024-09-28T22:35:04Z) - Analysis of Unstructured High-Density Crowded Scenes for Crowd Monitoring [55.2480439325792]
We are interested in developing an automated system for detection of organized movements in human crowds.
Computer vision algorithms can extract information from videos of crowded scenes.
We can estimate the number of participants in an organized cohort.
arXiv Detail & Related papers (2024-08-06T22:09:50Z) - Wireless Crowd Detection for Smart Overtourism Mitigation [50.031356998422815]
This chapter describes a low-cost approach to monitoring overtourism based on mobile devices' wireless activity.
The crowding sensors count the number of surrounding mobile devices, by detecting trace elements of wireless technologies.
They run detection programs for several technologies, and fingerprinting analysis results are only stored locally in an anonymized database.
arXiv Detail & Related papers (2024-02-14T13:20:24Z) - A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing.
Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data.
Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
arXiv Detail & Related papers (2023-09-27T14:38:16Z) - Guaranteed Discovery of Controllable Latent States with Multi-Step
Inverse Models [51.754160866582005]
Agent-Controllable State Discovery algorithm (AC-State)
Algorithm consists of a multi-step inverse model (predicting actions from distant observations) with an information bottleneck.
We demonstrate the discovery of controllable latent state in three domains: localizing a robot arm with distractions, exploring in a maze alongside other agents, and navigating in the Matterport house simulator.
arXiv Detail & Related papers (2022-07-17T17:06:52Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z) - WiFi-based Crowd Monitoring and Workspace Planning for COVID-19 Recovery [1.90365714903665]
This article introduces a novel IoT crowd monitoring solution which uses software defined networks (SDN) assisted WiFi access points as 24/7 sensors to monitor and analyze the use of physical space.
Prototypes and crowd behavior models are developed using over 500 million records captured on a university campus.
arXiv Detail & Related papers (2020-07-23T20:45:44Z) - Give more data, awareness and control to individual citizens, and they
will help COVID-19 containment [74.10257867142049]
Contact-tracing apps are being proposed for large scale adoption by many countries.
A centralized approach raises concerns about citizens' privacy and needlessly strong digital surveillance.
We advocate a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores"
arXiv Detail & Related papers (2020-04-10T20:30:37Z)
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.