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arXiv · 2303.09534

InCrowdFormer: On-Ground Pedestrian World Model From Egocentric Views

Mai Nishimura·Shohei Nobuhara·Ko Nishino
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Abstract

We introduce an on-ground Pedestrian World Model, a computational model that can predict how pedestrians move around an observer in the crowd on the ground plane, but from just the egocentric-views of the observer. Our model, InCrowdFormer, fully leverages the Transformer architecture by modeling pedestrian interaction and egocentric to top-down view transformation with attention, and autoregressively predicts on-ground positions of a variable number of people with an encoder-decoder architecture. We encode the uncertainties arising from unknown pedestrian heights with latent codes to predict the posterior distributions of pedestrian positions. We validate the effectiveness of InCrowdFormer on a novel prediction benchmark of real movements. The results show that InCrowdFormer accurately predicts the future coordination of pedestrians. To the best of our knowledge, InCrowdFormer is the first-of-its-kind pedestrian world model which we believe will benefit a wide range of egocentric-view applications including crowd navigation, tracking, and synthesis.

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Mai Nishimura, Shohei Nobuhara, Ko Nishino. 2023-03-16. InCrowdFormer: On-Ground Pedestrian World Model From Egocentric Views. https://arxiv.org/abs/2303.09534

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