RT Conference Proceedings
T1 An extension of GHMMs for environments with occlusions and automatic goal discovery for person trajectory prediction
A1 Pérez-Hurtado, Ignacio
A1 Capitán, Jesús
A1 Caballero, Fernando
A1 Merino, Luis
K1 Hidden Markov models
K1 Learning (artificial intelligence)
K1 Mobile robots
K1 Path planning
K1 Robot vision
K1 Proyecto TERESA
AB 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.
PB IEEE
SN 978-1-4673-9163-4
YR 2015
FD 2015
LK http://hdl.handle.net/10433/1659
UL http://hdl.handle.net/10433/1659
LA en
NO European Conference on Mobile Robots (ECMR), 2015
NO This work is partially funded by the EC-FP7 under grant agreement no.611153 (TERESA) and the project PAIS-MultiRobot, funded by the Junta deAndalucía (TIC-7390). I. Perez-Hurtado is also supported by the Postdoctoral Junior Grant 2013 co-funded by the Spanish Ministry of Economy andCompetitiveness and the Pablo de Olavide University.
NO Universidad Pablo de Olavide. Departamento de Deporte e Informática
DS RIO
RD May 2, 2026