A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
ACM
Abstract
Simulating crowds requires controlling a very large number of trajectories and is usually performed using crowd motion algorithms for which appropriate parameter values need to be found. The study of the relation between parametric values for simulation techniques and the quality of the resulting trajectories has been studied either through perceptual experiments or by comparison with real crowd trajectories. In this paper, we integrate both strategies. A quality metric, QF, is proposed to abstract from reference data while capturing the most salient features that affect the perception of trajectory realism. QF weights and combines cost functions that are based on several individual, local and global properties of trajectories. These trajectory features are selected from the literature and from interviews with experts. To validate the capacity of QF to capture perceived trajectory quality, we conduct an online experiment that demonstrates the high agreement between the automatic quality score and non-expert users. To further demonstrate the usefulness of QF , we use it in a data-free parameter tuning application able to tune any parametric microscopic crowd simulation model that outputs independent trajectories for characters. The learnt parameters for the tuned crowd motion model maintain the influence of the reference data which was used to weight the terms of QF.
Description
@inproceedings{10.1145:3480136,
booktitle = {Proceedings of the ACM on Computer Graphics and Interactive Techniques},
editor = {Narain, Rahul and Neff, Michael and Zordan, Victor},
title = {{A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation}},
author = {Daniel, Beatriz Cabrero and Marques, Ricardo and Hoyet, Ludovic and Pettré, Julien and Blat, Josep},
year = {2021},
publisher = {ACM},
ISSN = {2577-6193},
ISBN = {},
DOI = {10.1145/3480136}
}