Unsupervised Pedestrian Trajectory Prediction with Graph Neural Networks

Abstract

Trajectory prediction can aid in target tracking, automatic system navigation, social behavior prediction, analysis and other computer vision tasks. When people walk in crowded spaces such as sidewalks, subway and airports, etc., they naturally adjust their walking style according to the scene context and follow common social etiquette, such as maintaining separation and avoiding collisions. Accurate prediction of trajectories is a big challenge in a crowded scenario where interaction between targets may cause complex societal dynamics. Unlike the prediction of a single person trajectory, it is difficult to capture the real motion of multiple people by only considering the historical positions of each individual separately. Benefit from the recent success of graph neural networks, we propose a model called GNN-TP for pedestrian trajectory prediction. GNN-TP is a purely data-driven model that simultaneously infers the interactions between pedestrians in an unsupervised way and predicts their future trajectories jointly in crowded scenes. On the one hand, GNN-TP infers the interactions employing the observed historical trajectories. We transfer pedestrians' information on the graph-structured data and classify the interaction type based on edges' features. On the other hand, it learns the dynamical model and predicts future trajectories based on the inferred interactions and the observations. Extensive experiments show that our trajectory prediction model achieves efficient and state-of-the-art performance on several public datasets.