  Predicting the future trajectory of a person remains a challenging problem,
due to randomness and subjectivity of human movement. However, the moving
patterns of human in a constrained scenario typically conform to a limited
number of regularities to a certain extent, because of the scenario
restrictions and person-person or person-object interactivity. Thus, an
individual person in this scenario should follow one of the regularities as
well. In other words, a person's subsequent trajectory has likely been traveled
by others. Based on this hypothesis, we propose to forecast a person's future
trajectory by learning from the implicit scene regularities. We call the
regularities, inherently derived from the past dynamics of the people and the
environment in the scene, scene history. We categorize scene history
information into two types: historical group trajectory and
individual-surroundings interaction. To exploit these two types of information
for trajectory prediction, we propose a novel framework Scene History
Excavating Network (SHENet), where the scene history is leveraged in a simple
yet effective approach. In particular, we design two components: the group
trajectory bank module to extract representative group trajectories as the
candidate for future path, and the cross-modal interaction module to model the
interaction between individual past trajectory and its surroundings for
trajectory refinement. In addition, to mitigate the uncertainty in ground-truth
trajectory, caused by the aforementioned randomness and subjectivity of human
movement, we propose to include smoothness into the training process and
evaluation metrics. We conduct extensive evaluations to validate the efficacy
of our proposed framework on ETH, UCY, as well as a new, challenging benchmark
dataset PAV, demonstrating superior performance compared to state-of-the-art
methods.
