StarNet: Pedestrian Trajectory Prediction using Deep Neural Network in Star Topology

TL;DR

StarNet is a novel deep neural network model with a star topology that effectively captures complex pedestrian interactions for trajectory prediction, outperforming existing methods in accuracy and computational efficiency.

Contribution

The paper introduces StarNet, a new model with a star topology that models collective pedestrian interactions efficiently and accurately, surpassing prior pairwise interaction methods.

Findings

StarNet achieves higher accuracy than state-of-the-art models.

StarNet is more computationally efficient with linear complexity.

Experimental results on public datasets validate the effectiveness of StarNet.

Abstract

Pedestrian trajectory prediction is crucial for many important applications. This problem is a great challenge because of complicated interactions among pedestrians. Previous methods model only the pairwise interactions between pedestrians, which not only oversimplifies the interactions among pedestrians but also is computationally inefficient. In this paper, we propose a novel model StarNet to deal with these issues. StarNet has a star topology which includes a unique hub network and multiple host networks. The hub network takes observed trajectories of all pedestrians to produce a comprehensive description of the interpersonal interactions. Then the host networks, each of which corresponds to one pedestrian, consult the description and predict future trajectories. The star topology gives StarNet two advantages over conventional models. First, StarNet is able to consider the collective influence among all pedestrians in the hub network, making more accurate predictions. Second, StarNet is computationally efficient since the number of host network is linear to the number of pedestrians. Experiments on multiple public datasets demonstrate that StarNet outperforms multiple state-of-the-arts by a large margin in terms of both accuracy and efficiency.