Grouptron: 群れを考慮した動的マルチスケールグラフ畳み込みネットワークによる密集群衆の軌道予測
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting
歩行者の群れを時空間クラスタリングで検出し、個人・群れ・シーン各レベルのグラフニューラルネットで動態を符号化して軌道を予測する手法を提案し、FDEを9.3%改善した。
著者: Rui Zhou, Hongyu Zhou, Huidong Gao, Masayoshi Tomizuka, Jiachen Li, Zhuo Xu
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge. Recent advances in using data-driven approaches have achieved significant improvements in terms of prediction accuracy. However, the lack of group-aware analysis has limited the performance of forecasting models. This is especially nonnegligible in highly crowded scenes, where pedestrians are moving in groups and the interactions between groups are extremely complex and dynamic. In this paper, we present Grouptron, a multi-scale dynamic forecasting framework that leverages pedestrian group detection and utilizes individual-level, group-level and scene-level information for better understanding and representation of the scenes. Our approach employs spatio-temporal clustering algorithms to identify pedestrian groups, creates spatio-temporal graphs at the individual, group, and scene levels. It then uses graph neural networks to encode dynamics at different scales and aggregate the embeddings for trajectory prediction. We conducted extensive comparisons and ablation experiments to demonstrate the effectiveness of our approach. Our method achieves 9.3% decrease in final displacement error (FDE) compared with state-of-the-art methods on ETH/UCY benchmark datasets, and 16.1% decrease in FDE in more crowded scenes where extensive human group interactions are more frequently present.
