WebTo support more efficient and accurate trajectory predictions, we propose a novel CNN-based spatial-temporal graph framework GraphTCN, which models the spatial interactions as social graphs and captures the spatio-temporal interactions with a modified temporal convolutional network. In contrast to conventional models, both the spatial and ... Web衡量两条轨迹之间的相似度，并且这些轨迹数据是有定位误差和零星采样问题. 1 Intro 1.1 background. 随着物联网设备和定位技术的发展，会产生许多时空相似度很高的轨迹，例如： 单个个体被多个定位系统采集

Temporal Interaction Modeling for Human Trajectory Prediction

WebTable 1: Quantitative results of our GraphTCN compared with baseline approaches. Evaluation metrics are reported in ADE / FDE in meters (the lower numerical result is better). Our GraphTCN achieves significantly better predictions than other baselines. - "GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction" WebGraphTCN 3 nodes in the graph represent agents, and edges between two agents denote their geometric relation. EGAT then learns the adjacency matrix, i.e., the spatial in-teraction, of the graph adaptively. Together, the spatial and temporal modules of GraphTCN support more e ective and e cient modeling of the interactions can pepto bismol help with gas

GATraj: A Graph- and Attention-based Multi-Agent Trajectory …

WebOur GraphTCN framework is introduced in Section 3. Then in Section 4, results of GraphTCN measured in both accu-racy and efficiency are compared with state-of-the-art ap-proaches. Finally, Section 5 concludes the paper. 2. Related Work Human-Human Interactions. Research in the crowd in-teraction model can be traced back to the Social … WebTemporal Interaction Modeling for Human Trajectory Prediction WebImplement GraphTCN with how-to, Q&A, fixes, code snippets. kandi ratings - Low support, No Bugs, No Vulnerabilities. No License, Build not available. flameproof pump