Context-aware human trajectories prediction via latent variational model
| Autor: | Hichem Sahli, Meshia Cédric Oveneke, Mitchel Alioscha-Perez, Abel Diaz Berenguer |
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| Přispěvatelé: | Faculty of Engineering, Electronics and Informatics, Audio Visual Signal Processing |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Context model
Computer science business.industry Stochastic process Context (language use) 02 engineering and technology Interpersonal communication Machine learning computer.software_genre ENCODE 0202 electrical engineering electronic engineering information engineering Media Technology Trajectory 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering Latent variable model business computer Decoding methods |
| Popis: | 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 article 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. |
| Databáze: | OpenAIRE |
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