  Predicting pedestrian motion is essential for developing socially-aware
robots that interact in a crowded environment. While the natural visual
perspective for a social interaction setting is an egocentric view, the
majority of existing work in trajectory prediction therein has been
investigated purely in the top-down trajectory space. To support first-person
view trajectory prediction research, we present T2FPV, a method for
constructing high-fidelity first-person view (FPV) datasets given a real-world,
top-down trajectory dataset; we showcase our approach on the ETH/UCY pedestrian
dataset to generate the egocentric visual data of all interacting pedestrians,
creating the T2FPV-ETH dataset. In this setting, FPV-specific errors arise due
to imperfect detection and tracking, occlusions, and field-of-view (FOV)
limitations of the camera. To address these errors, we propose CoFE, a module
that further refines the imputation of missing data in an end-to-end manner
with trajectory forecasting algorithms. Our method reduces the impact of such
FPV errors on downstream prediction performance, decreasing displacement error
by more than 10% on average. To facilitate research engagement, we release our
T2FPV-ETH dataset and software tools.
