Dynamic phase and group detection in pedestrian crowd data using multiplex visibility graphs

Research output: Contribution to journalArticle

1 Citation (Scopus)
20 Downloads (Pure)

Abstract

We study pedestrian crowd dynamics and the detection of groups in a scene. We propose a novel method to analyse pedestrian trajectories by translating them to multiplex networks, whose properties can be studied using the tools of graph theory. Our results show that simple measures on the resulting multiplex graphs accurately reflect both the global dynamics and local clustering within scenes. Publisher statement: This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits users to copy, distribute and transmit the work for non-commercial purposes providing it is properly cited.
Original languageEnglish
Pages (from-to)410–419
JournalProcedia Computer Science
Volume53
DOIs
Publication statusPublished - 2015

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Bibliographical note

Paper presented at the INNS Conference on Big Data 2015 Program San Francisco, CA, USA 8-10 August 2015.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits users to copy, distribute and transmit the work for non-commercial purposes providing it is properly cited.

Keywords

  • crowd data
  • dynamical systems
  • visibility graph clustering

Cite this

Dynamic phase and group detection in pedestrian crowd data using multiplex visibility graphs. / Stephen, Colin.

In: Procedia Computer Science, Vol. 53, 2015, p. 410–419.

Research output: Contribution to journalArticle

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