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Predicting pedestrian motion is essential for developing socially-awarerobots that interact in a crowded environment. While the natural visualperspective for a social interaction setting is an egocentric view, themajority of existing work in trajectory prediction therein has beeninvestigated purely in the top-down trajectory space. To support first-personview trajectory prediction research, we present T2FPV, a method forconstructing high-fidelity first-person view (FPV) datasets given a real-world,top-down trajectory dataset; we showcase our approach on the ETH/UCY pedestriandataset to generate the egocentric visual data of all interacting pedestrians,creating the T2FPV-ETH dataset. In this setting, FPV-specific errors arise dueto imperfect detection and tracking, occlusions, and field-of-view (FOV)limitations of the camera. To address these errors, we propose CoFE, a modulethat further refines the imputation of missing data in an end-to-end mannerwith trajectory forecasting algorithms. Our method reduces the impact of suchFPV errors on downstream prediction performance, decreasing displacement errorby more than 10% on average. To facilitate research engagement, we release ourT2FPV-ETH dataset and software tools.

T2FPV: Dataset and Method for Correcting First-Person View Errors in Pedestrian Trajectory Prediction