  Learning-based approaches to modeling crowd motion have become increasingly
successful but require training and evaluation on large datasets, coupled with
complex model selection and parameter tuning. To circumvent this tremendously
time-consuming process, we propose a novel scoring method, which characterizes
generalization of models trained on source crowd scenarios and applied to
target crowd scenarios using a training-free, model-agnostic Interaction +
Diversity Quantification score, ISDQ. The Interaction component aims to
characterize the difficulty of scenario domains, while the diversity of a
scenario domain is captured in the Diversity score. Both scores can be computed
in a computation tractable manner. Our experimental results validate the
efficacy of the proposed method on several simulated and real-world
(source,target) generalization tasks, demonstrating its potential to select
optimal domain pairs before training and testing a model.
