T2FPV: 一人称視点の歩行者軌道予測における誤差補正のためのデータセットと手法
T2FPV: Dataset and Method for Correcting First-Person View Errors in Pedestrian Trajectory Prediction
実世界の俯瞰軌道データセットから一人称視点の歩行者軌道予測データセットを構築する手法を提案し、検出・追跡・遮蔽・視野制限による誤差を補正するモジュールCoFEを開発した。
著者: Benjamin Stoler, Meghdeep Jana, Soonmin Hwang, Jean Oh
分類: cs.CV, cs.RO
原文アブストラクト
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.
