T2FPV: Constructing High-Fidelity First-Person View Datasets From Real-World Pedestrian Trajectories

• URL: http://arxiv.org/abs/2209.11294v1
• Date: Thu, 22 Sep 2022 20:14:43 GMT
• Title: T2FPV: Constructing High-Fidelity First-Person View Datasets From Real-World Pedestrian Trajectories
• Authors: Benjamin Stoler, Meghdeep Jana, Soonmin Hwang, Jean Oh
• Abstract summary: We present T2FPV, a method for constructing high-fidelity first-person view 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.
• Score: 9.44806128120871
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 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 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. We report that the bird's-eye view assumption used in the original ETH/UCY dataset, i.e., an agent can observe everyone in the scene with perfect information, does not hold in the first-person views; only a fraction of agents are fully visible during each 20-timestep scene used commonly in existing work. We evaluate existing trajectory prediction approaches under varying levels of realistic perception -- displacement errors suffer a 356% increase compared to the top-down, perfect information setting. To promote research in first-person view trajectory prediction, we release our T2FPV-ETH dataset and software tools.

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