A Data-Driven Framework for Visual Crowd Analysis

Panayiotis Charalambous, Ioannis Karamouzas, Stephen J Guy, Yiorgos Chrysanthou

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

We 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.

Original languageEnglish (US)
Pages (from-to)41-50
Number of pages10
JournalComputer Graphics Forum
Volume33
Issue number7
DOIs
StatePublished - Oct 1 2014

Bibliographical note

Publisher Copyright:
© 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

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