  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.
