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Context-aware human trajectories prediction via latent variational model

This publication appears in: IEEE Transactions on Circuits and Systems for Video Technology

Authors: A. Díaz Berenguer, M. Perez Gonzalez, M. Oveneke and H. Sahli

Volume: Early Access

Publication Date: Aug. 2020


Abstract:

Understanding human-contextual interaction to predict human trajectories is a challenging problem. Most of previous trajectory prediction approaches focused on modeling the human-human interaction located in a near neighborhood and neglected the influence of individuals which are farther in the scene as well as the scene layout. To alleviate these limitations, in this paper we propose a model to address pedestrian trajectory prediction using a latent variable model aware of the human-contextual interaction. Our proposal relies on contextual information that influences the trajectory of pedestrians to encode human-contextual interaction. We model the uncertainty about future trajectories via latent variational model and captures relative interpersonal influences among all the subjects within the scene and their interaction with the scene layout to decode their trajectories. In extensive experiments, on publicly available datasets, it is shown that using contextual information and latent variational model, our trajectory prediction model achieves competitive results compared to state-of-the-art models.

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Mr. Abel Díaz Berenguer

+32 (0)02 629 102

aberengu@etrovub.be

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Prof. Hichem Sahli

+32 (0)02 629 291

hsahli@etrovub.be

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