mlsanityData to 7 Oct 2026

Geometric Graph Neural Network Modeling of Human Interactions in Crowded Environments

arXiv 2410.17409 ↗First posted 22 Oct 2024cs.LG cs.AI cs.CVcited by 1

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Abstract (arXiv)

Modeling human trajectories in crowded environments is challenging due to the complex nature of pedestrian behavior and interactions. This paper proposes a geometric graph neural network (GNN) architecture that integrates domain knowledge from psychological studies to model pedestrian interactions and predict future trajectories. Unlike prior studies using complete graphs, we define interaction neighborhoods using pedestrians' field of view, motion direction, and distance-based kernel functions to construct graph representations of crowds. Evaluations across multiple datasets demonstrate improved prediction accuracy through reduced average and final displacement error metrics. Our findings underscore the importance of integrating domain knowledge with data-driven approaches for effective modeling of human interactions in crowds.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 22 Oct 2024.

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10 of 21 references.

References · 10

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
  3. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  4. 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 324
  5. 2019Human Motion Trajectory Prediction: A Surveycited by 231
  6. 2019Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networkscited by 228
  7. 2021Core Challenges of Social Robot Navigation: A Surveycited by 97
  8. 2021A Survey on Graph Structure Learning: Progress and Opportunitiescited by 70
  9. 2021Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approachescited by 30
  10. 2021SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Predictioncited by 19

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Citing papers per year: 2025: 1.

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  1. 2025Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environmentscited by 0