Show simple item record

dc.contributor.authorCharalambous, Panayiotisen
dc.contributor.authorKaramouzas, I.en
dc.contributor.authorGuy, S. J.en
dc.contributor.authorChrysanthou, Yiorgos L.en
dc.creatorCharalambous, Panayiotisen
dc.creatorKaramouzas, I.en
dc.creatorGuy, S. J.en
dc.creatorChrysanthou, Yiorgos L.en
dc.date.accessioned2019-11-13T10:38:52Z
dc.date.available2019-11-13T10:38:52Z
dc.date.issued2014
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/53683
dc.description.abstractWe present a novel approach for analyzing the quality of multi-agent crowd simulation algorithms. Our approach is data-driven, taking as input a set of user-defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state-of-the-art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per-agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real-time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely-used methods, including a simulation from a commercial game. © 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.en
dc.sourceComputer Graphics Forumen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84939424287&doi=10.1111%2fcgf.12472&partnerID=40&md5=e06f4b25b47c2d3a9997ade221f8cc1e
dc.subjectStatisticsen
dc.subjectMulti agent systemsen
dc.subjectAlgorithmsen
dc.subjectPareto principleen
dc.subjectPrincipal component analysisen
dc.subjectAnalysis of simulationsen
dc.subjectEvaluation criteriaen
dc.subjectEvaluation metricsen
dc.subjectOutlier detection algorithmen
dc.subjectPareto-optimalityen
dc.subjectPrinciple component analysisen
dc.subjectReal time performanceen
dc.subjectUser-defined metricsen
dc.titleA Data-Driven Framework for Visual Crowd Analysisen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1111/cgf.12472
dc.description.volume33
dc.description.issue7
dc.description.startingpage41
dc.description.endingpage50
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :8</p>en
dc.source.abbreviationComput.Graphics Forumen
dc.contributor.orcidChrysanthou, Yiorgos L. [0000-0001-5136-8890]
dc.contributor.orcidCharalambous, Panayiotis [0000-0002-7230-5132]
dc.gnosis.orcid0000-0001-5136-8890
dc.gnosis.orcid0000-0002-7230-5132


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record