The paper presents a method for predicting the future trajectories of a person navigating through complex environments, using an egocentric perspective. The key aspects are:
Visual Memory Representation: The method constructs a compact, panoramic representation of the surrounding environment from the egocentric camera view, encoding appearance, geometry, and semantic information. This "visual memory" provides a rich context for trajectory prediction.
Diffusion Model for Trajectory Prediction: A diffusion model is employed to predict a distribution of potential future trajectories, conditioned on the person's past trajectory and the visual memory of the environment. The diffusion model starts from random noise and performs denoising steps to generate the predicted trajectory sequence.
Hybrid Generation Technique: To enable real-time inference, a hybrid generation technique is introduced that combines the strengths of DDIM and DDPM approaches, providing a balance between generation quality and speed.
Egocentric Navigation Dataset: The authors collected a comprehensive dataset of egocentric walking scenes, including diverse indoor and outdoor environments, to facilitate research in this domain.
The evaluation shows that the proposed method outperforms baseline approaches on key metrics such as collision avoidance and trajectory mode coverage, by effectively leveraging the scene context provided by the visual memory.
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by Weizhuo Wang... at arxiv.org 03-29-2024
https://arxiv.org/pdf/2403.19026.pdfDeeper Inquiries