Equivariant Graph Learning for High-density Crowd Trajectories Modeling
Abstract: Understanding the high-density crowd dynamics of urbanization plays an important role in architectural design and urban planning, preventing the occurrence of crowd crush. Most traditional methods rely on formulas designed based on expert knowledge, which are inflexible and incomplete to model complex real-world crowd trajectories. To address the issue, recent studies propose to simulate crowds via data-driven models. However, these models fail to learn the inherent symmetry of high-density crowd trajectories, leading to insufficient generalization ability. For example, existing models can not predict left-to-right trajectories by learning right-to-left trajectories, even though they share similar patterns. In this work, we propose a novel Equivariant Graph Learning framework for high-density crowd dynamic modeling, called CrowdEGL. It utilizes an additional objective to encourage models to predict the transformed output given the input under the same transformation. We summarize three types of transformation groups, which are determined by the symmetry of environments. To explicitly incorporate these augmented data, a multi-channel GNN is employed to learn the latent graph embedding of pedestrian patterns. Finally, to model dense crowd interactions, future positions of original and transformed inputs are obtained by multiple independent graph decoders. Extensive experiments on 8 datasets from 5 different environments show that CrowdEGL outperforms existing models by a large margin.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Francisco_J._R._Ruiz1
Submission Number: 2890
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