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 this https URL.

本文提出了一种基于元路径的特征提取方法，用于改善现有 ST-graph 模型在交通预测、人体动作建模等领域表现不佳的问题，并在行人轨迹预测任务中应用，实现了比传统方法高 32% 的预测精度及更为社交化的轨迹。

基于时空图的行人轨迹预测的元路径分析

Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction

A group of persons can be analyzed at various semantic levels such as
individual actions, their interactions, and the activity of the entire group.
In this paper, we propose a structural recurrent neural network (SRNN) that
uses a series of interconnected RNNs to jointly capture the actions of
individuals, their interactions, as well as the group activity. While previous
structural recurrent neural networks assumed that the number of nodes and edges
is constant, we use a grid pooling layer to address the fact that the number of
individuals in a group can vary. We evaluate two variants of the structural
recurrent neural network on the Volleyball Dataset.

本文提出结构递归神经网络 (SRNN)，用于捕捉个体动作、个体间互动、以及整个群体活动。相比于之前的方法，本文使用网格池化层来解决群体中的个体数量变化的问题，同时在排球数据集上评估了两种 SRNN 变体的性能。