  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.
