2022/11/02 by Gang Qiao, Qiao, Gang, Kaidong Hu +11
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2211.00817
openalex publication_date 2022/11/02 · openalex created_date 2023/02/14 · openalex updated_date 2026/07/28
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.