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Simulating crowds requires controlling a very large number of trajectoriesand is usually performed using crowd motion algorithms for which appropriateparameter values need to be found. The study of the relation between parametricvalues for simulation techniques and the quality of the resulting trajectorieshas been studied either through perceptual experiments or by comparison withreal crowd trajectories. In this paper, we integrate both strategies. A qualitymetric, QF, is proposed to abstract from reference data while capturing themost salient features that affect the perception of trajectory realism. QFweights and combines cost functions that are based on several individual, localand global properties of trajectories. These trajectory features are selectedfrom the literature and from interviews with experts. To validate the capacityof QF to capture perceived trajectory quality, we conduct an online experimentthat demonstrates the high agreement between the automatic quality score andnon-expert users. To further demonstrate the usefulness of QF, we use it in adata-free parameter tuning application able to tune any parametric microscopiccrowd simulation model that outputs independent trajectories for characters.The learnt parameters for the tuned crowd motion model maintain the influenceof the reference data which was used to weight the terms of QF.

A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation