2022/09/22 by Benjamin Stoler, Meghdeep Jana, Stoler, Benjamin +5 · 1 citation
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO) #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2209.11294
openalex publication_date 2022/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.