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

TL;DR

This paper introduces MESRNN, a novel framework utilizing meta-path features in spatio-temporal graphs to significantly enhance pedestrian trajectory prediction accuracy and social compliance over existing methods.

Contribution

The paper proposes a generic, scalable meta-path based feature extraction method integrated into a structural recurrent neural network for improved spatio-temporal prediction tasks.

Findings

Over 32% improvement in long-term trajectory prediction accuracy

More socially compliant trajectories in dense crowds

Outperforms state-of-the-art ST-graph methods on standard datasets

Abstract

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.