Steen, M.R. van [Promotor]Bal, H.E. [Promotor

Martella, C.

English

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VU Research PortalCrowd TexturesMartella, C.2017document versionPublisher's PDF, also known as Version of recordLink to publication in VU Research Portalcitation for published version (APA)Martella, C. (2017). Crowd Textures: From Sensing Proximity to Understanding Crowd Behavior.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 13. Dec. 2021The myriad of sensors present on mobile, pervasive, and ubiquitous devices we wear and install around us is allowing us to collect an unprecedented number of signals about our behavior in the real world, in addition to the massive volumes of data we already collect about our online activities. Spatio-temporal proximity is one of these signals and it is the topic of this dissertation.Many of our social behaviors and interactions are characterized by a spatio-temporal nature. When we mingle, when we interact with some objects or appliances, when we share an office or a space with colleagues and family, when we queue on the way to a train, we generate a network of proximity relationships between us and the objects and people around. We call this network of proximity relationships the texture of a crowd.In this dissertation we present a model for crowd textures based on spatio-temporal proximity, a set of techniques that can be used to measure and collect textures reliably at large scale, and a number of mining algorithms that can be used by crowd manag-ers to recognize and visualize group and crowd behavior, with the final goal of ensuring the safety and comfort of the crowd.Crowd TexturesClaudio MartellaCrowd TexturesFrom Sensing Proximityto Understanding Crowd Behavior

Crowd Textures:From Sensing Proximity to Understanding Crowd Behavior

DSpace at VU

The myriad of sensors present on mobile, pervasive, 
and ubiquitous devices we wear and install around us 
is allowing us to collect an unprecedented number of 
signals about our behavior in the real world, in 
addition to the massive volumes of data we already 
collect about our online activities. Spatio-temporal 
proximity is one of these signals and it is the topic of 
this dissertation.
Many of our social behaviors and interactions are 
characterized by a spatio-temporal nature. When we 
mingle, when we interact with some objects or 
appliances, when we share an office or a space with 
colleagues and family, when we queue on the way to a 
train, we generate a network of proximity relationships 
between us and the objects and people around. We 
call this network of proximity relationships the texture 
of a crowd.
In this dissertation we present a model for crowd 
textures based on spatio-temporal proximity, a set of 
techniques that can be used to measure and collect 
textures reliably at large scale, and a number of 
mining algorithms that can be used by crowd manag-
ers to recognize and visualize group and crowd 
behavior, with the final goal of ensuring the safety and 
comfort of the crowd.
Crowd Textures
Claudio Martella
Crowd Textures
From Sensing Proximity
to Understanding Crowd Behavior


Crowd Textures: From Sensing Proximity to Understanding Crowd Behavior

https://research.vu.nl/ws/files/41926126/title%20page.pdf

Crowd Textures:From Sensing Proximity to Understanding Crowd Behavior

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