Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction
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DOI：
10.1109/icra46639.2022.9811632
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Aamir Hasan;Pranav Sriram;K. Driggs-Campbell
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文献类型：
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作者：
Aamir Hasan;Pranav Sriram;K. Driggs-Campbell
时空图（ST-图）已被用于建模时间序列任务，如交通预测，人体运动建模和动作识别。ST图的高级结构和相应的功能导致了传统架构的性能提高。然而，目前的方法往往受到简单功能的限制，尽管完整的图结构提供了丰富的信息，这导致下游任务的效率低下和次优性能。我们建议使用来自元路径的特征，在ST图中遍历不同类型的边，以提高结构递归神经网络的性能。在本文中，我们提出了元路径增强结构递归神经网络（MESRNN），一个通用的框架，可以应用于任何时空任务，在一个简单的和可扩展的方式。我们采用MESRNN行人轨迹预测，利用这些元路径为基础的功能，以捕捉行人在不同的时间和空间点的轨迹之间的关系。我们将MESRNN与标准数据集上最先进的ST图方法进行比较，以显示元路径信息提供的性能提升。该模型在长时间范围内的轨迹预测中始终优于基线超过32%，并在密集人群中产生更符合社会要求的轨迹。欲了解更多信息，请参阅项目网站https://sites.google.com/illinois.edu/mesrnn/home。
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.