Perceptual effects of scene context and viewpoint for virtual pedestrian crowds

Ennis, Cathy; Peters, Christopher; O’Sullivan, Carol

doi:10.1145/1870076.1870078

Cited by 38 publications

(30 citation statements)

References 15 publications

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“…Qualitative methods for crowd evaluation have been proposed and include visual comparison [14], [15] and perceptual experiments [16], [17], [18]. Quantitative methods fall into two main categories: model-based [19], [20] and data-driven [21], [22], [23], [24].…”

Section: Related Workmentioning

confidence: 99%

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Wang

Ondřej

O’Sullivan

2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation has been an active and important area for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose the first approach based on finding semantic information represented by latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd behavior. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly. Detailed evaluations and comparisons with existing metrics show that our method is a good alternative for comparing crowd data at a different level and also works with more types of data, holds fewer assumptions and is more robust to noise.

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Section: Related Workmentioning

confidence: 99%

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Wang

Ondřej

O’Sullivan

2017

IEEE Trans. Visual. Comput. Graphics

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“…Qualitative methods for crowd evaluation have been proposed and include visual comparison [Kim et al 2013;Lemercier et al 2012] and perceptual experiments [McDonnell et al 2008;Guy et al 2011;Ennis et al 2011]. Quantitative methods fall into two main categories: model-based [Kim et al 2012;Golas et al 2013] and data-driven [Singh et al 2009;Ju et al 2010;Kapadia et al 2011;Musse et al 2012].…”

Section: Related Workmentioning

confidence: 99%

Path patterns

Wang

Ondřej

O’Sullivan

2016

Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games

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Crowd simulation has been an active and important area of research in the field of interactive 3D graphics for several decades. However, only recently has there been an increased focus on evaluating the fidelity of the results with respect to real-world situations. The focus to date has been on analyzing the properties of low-level features such as pedestrian trajectories, or global features such as crowd densities. We propose a new approach based on finding latent Path Patterns in both real and simulated data in order to analyze and compare them. Unsupervised clustering by non-parametric Bayesian inference is used to learn the patterns, which themselves provide a rich visualization of the crowd's behaviour. To this end, we present a new Stochastic Variational Dual Hierarchical Dirichlet Process (SV-DHDP) model. The fidelity of the patterns is then computed with respect to a reference, thus allowing the outputs of different algorithms to be compared with each other and/or with real data accordingly.

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“…For example, the work of evaluates crowd simulations based on quantifying presence in virtual environments. Similarly, [Ennis et al 2011;Jarabo et al 2012] measure the perceptual effects of factors such as illumination, camera position and orientation on the perceived fidelity of movement in crowds. In a similar spirit, we also perform a pilot study to measure the correlation between our numerical similarity metric and the perceptual similarity of crowd motion to the validation data.…”

Section: Validating Crowd Simulatorsmentioning

confidence: 99%

A statistical similarity measure for aggregate crowd dynamics

et al. 2012

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Figure 1: A comparison between a rendering of real-world crowd data (a), and stills from three different simulation algorithms applied to the same scenario (b-d). Our entropy metric is used to measure the similarity of simulation algorithm to real-world data. A small value of the metric, as in (d), indicates a better match to the data. Differences between the simulations are highlighted with circles. AbstractWe present an information-theoretic method to measure the similarity between a given set of observed, real-world data and visual simulation technique for aggregate crowd motions of a complex system consisting of many individual agents. This metric uses a two-step process to quantify a simulator's ability to reproduce the collective behaviors of the whole system, as observed in the recorded realworld data. First, Bayesian inference is used to estimate the simulation states which best correspond to the observed data, then a maximum likelihood estimator is used to approximate the prediction errors. This process is iterated using the EM-algorithm to produce a robust, statistical estimate of the magnitude of the prediction error as measured by its entropy (smaller is better). This metric serves as a simulator-to-data similarity measurement. We evaluated the metric in terms of robustness to sensor noise, consistency across different datasets and simulation methods, and correlation to perceptual metrics.

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Perceptual effects of scene context and viewpoint for virtual pedestrian crowds

Cited by 38 publications

References 15 publications

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Trending Paths: A New Semantic-Level Metric for Comparing Simulated and Real Crowd Data

Path patterns

A statistical similarity measure for aggregate crowd dynamics

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