Ponencia
An extension of GHMMs for environments with occlusions and automatic goal discovery for person trajectory prediction
Autor/es | Pérez Hurtado de Mendoza, Ignacio
Capitán Fernández, Jesús Caballero, Fernando Merino, Luis |
Director | |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2015 |
Fecha de depósito | 2019-07-05 |
Publicado en |
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ISBN/ISSN | 978-1-4673-9163-4 |
Resumen | Robots navigating in a social way should use some
knowledge about common motion patterns of people in the
environment. Moreover, it is known that people move intending
to reach certain points of interest, and machine ... Robots navigating in a social way should use some knowledge about common motion patterns of people in the environment. Moreover, it is known that people move intending to reach certain points of interest, and machine learning techniques have been widely used for acquiring this knowledge by observation. Learning algorithms such as Growing Hidden Markov Models (GHMMs) usually assume that points of interest are located at the end of human trajectories, but complete trajectories cannot always be observed by a mobile robot due to occlusions and people going out of sensor range. This paper extends GHMMs to deal with partial observed trajectories where people’s goals are not known a priori. A novel technique based on hypothesis testing is also used to discover the points of interest (goals) in the environment. The approach is validated by predicting people’s motion in three different datasets. |
Cita | Pérez Hurtado de Mendoza, I., Capitán, J., Caballero, F. y Merino, L. (2015). An extension of GHMMs for environments with occlusions and automatic goal discovery for person trajectory prediction. En ECMR 2015: European Conference on Mobile Robots Lincoln, UK: IEEE Computer Society. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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An extension of GHMMs.pdf | 1.575Mb | [PDF] | Ver/ | |