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A Data‐Driven Framework for Visual Crowd Analysis
Author(s) -
Charalambous Panayiotis,
Karamouzas Ioannis,
Guy Stephen J.,
Chrysanthou Yiorgos
Publication year - 2014
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12472
Subject(s) - computer science , crowds , anomaly detection , outlier , exploit , crowd simulation , set (abstract data type) , data mining , interactive visual analysis , visualization , component (thermodynamics) , pareto principle , machine learning , artificial intelligence , basis (linear algebra) , synthetic data , visual analytics , operations management , physics , computer security , mathematics , economics , programming language , thermodynamics , geometry
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.