  Most pedestrian trajectory prediction methods rely on a huge amount of
trajectories annotation, which is time-consuming and expensive. Moreover, a
well-trained model may not effectively generalize to a new scenario captured by
another camera. Therefore, it is desirable to adapt the model trained on an
annotated source domain to the target domain. To achieve domain adaptation for
trajectory prediction, we propose a Cross-domain Trajectory Prediction Network
(CTP-Net). In this framework, encoders are used in both domains to encode the
observed trajectories, then their features are aligned by a cross-domain
feature discriminator. Further, considering the consistency between the
observed and the predicted trajectories, a target domain offset discriminator
is utilized to adversarially regularize the future trajectory predictions to be
in line with the observed trajectories. Extensive experiments demonstrate the
effectiveness of our method on domain adaptation for pedestrian trajectory
prediction.
