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

An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction