A Data-Driven Framework for Visual Crowd Analysis

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dc.contributor.authorCharalambous, Panayiotisen_US
dc.contributor.authorKaramouzas, Ioannisen_US
dc.contributor.authorGuy, Stephen J.en_US
dc.contributor.authorChrysanthou, Yiorgosen_US
dc.contributor.editorJ. Keyser, Y. J. Kim, and P. Wonkaen_US
dc.date.accessioned2015-03-03T12:50:55Z
dc.date.available2015-03-03T12:50:55Z
dc.date.issued2014en_US
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.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12472en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12472en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleA Data-Driven Framework for Visual Crowd Analysisen_US
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