Relational Subgraph for Graph-based Path Prediction

Abstract

Path prediction methods using graph convolutional networks (GCNs) that represent pedestrians' relationships by graphs have been proposed. These GCN-based methods consider only the distance information for the relationship between pedestrians, and the visibility state and other relationships are not taken into account. In this paper, we propose a path prediction method that represents the detailed relationship between pedestrians by introducing relational subgraphs. Each subgraph is constructed on different relationships. The proposed method inputs these relational subgraphs and the distance graph into GCNs and we extract features. Then, the features are input to a temporal convolutional network, which outputs multivariate Gaussian parameters to predict the future path. The experimental results with ETH and UCY datasets show that the proposed method outperforms the conventional method using only the distance information.