A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation
Beatriz Cabrero Daniel, Ricardo Marques, Ludovic Hoyet, Julien, Pettr\'e, Josep Blat

TL;DR
This paper introduces QF, a perceptually-validated metric for evaluating crowd trajectory quality, which effectively correlates with human perception and aids in parameter tuning of crowd simulation models.
Contribution
It proposes a new quality metric, QF, that combines multiple trajectory features and validates its effectiveness through perceptual experiments and practical parameter tuning.
Findings
QF correlates highly with human perception of trajectory realism.
QF enables effective data-free parameter tuning for crowd simulation models.
The metric captures salient features influencing perceived trajectory quality.
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…
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