用于行人轨迹预测的时空图元路径分析
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction
AI总结：
提出MESRNN框架，利用时空图中的元路径特征增强Structural RNN，应用于行人轨迹预测，在长时间跨度预测上性能提升超32%，并生成更具社会合规性的轨迹。
摘要：
时空图（ST-graphs）已被用于对交通预测、人体运动建模和动作识别等时间序列任务进行建模。来自ST-graphs的高层结构及相应特征带来了优于传统架构的性能提升。然而，尽管完整的图结构提供了丰富的信息，当前方法往往受限于简单的特征，这导致了下游任务中的低效和次优性能。我们提出在ST-graphs中使用源自元路径（跨越不同类型边的游走）的特征，以提升Structural Recurrent Neural Network的性能。在本文中，我们提出了Meta-path Enhanced Structural Recurrent Neural Network（MESRNN），这是一个可以简单且可扩展地应用于任何时空任务的通用框架。我们将MESRNN应用于行人轨迹预测，利用这些基于元路径的特征来捕捉行人在不同时间与空间点的轨迹之间的关系。我们在标准数据集上将我们的MESRNN与最先进的ST-graph方法进行比较，以展示元路径信息带来的性能提升。所提出的模型在长时间跨度的轨迹预测中始终优于基线方法超过32%，并在密集人群中产生更具社会合规性的轨迹。更多信息请访问项目网站 https://sites.google.com/illinois.edu/mesrnn/home。
英文摘要：
Spatio-temporal graphs (ST-graphs) have been used to model time series tasks such as traffic forecasting, human motion modeling, and action recognition. The high-level structure and corresponding features from ST-graphs have led to improved performance over traditional architectures. However, current methods tend to be limited by simple features, despite the rich information provided by the full graph structure, which leads to inefficiencies and suboptimal performance in downstream tasks. We propose the use of features derived from meta-paths, walks across different types of edges, in ST-graphs to improve the performance of Structural Recurrent Neural Network. In this paper, we present the Meta-path Enhanced Structural Recurrent Neural Network (MESRNN), a generic framework that can be applied to any spatio-temporal task in a simple and scalable manner. We employ MESRNN for pedestrian trajectory prediction, utilizing these meta-path based features to capture the relationships between the trajectories of pedestrians at different points in time and space. We compare our MESRNN against state-of-the-art ST-graph methods on standard datasets to show the performance boost provided by meta-path information. The proposed model consistently outperforms the baselines in trajectory prediction over long time horizons by over 32\%, and produces more socially compliant trajectories in dense crowds. For more information please refer to the project website at https://sites.google.com/illinois.edu/mesrnn/home.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
