Show simple item record

dc.contributor.authorPérez-Hurtado, Ignacio
dc.contributor.authorCapitán, Jesús
dc.contributor.authorCaballero, Fernando
dc.contributor.authorMerino, Luis 
dc.date.accessioned2016-03-14T16:32:33Z
dc.date.available2016-03-14T16:32:33Z
dc.date.issued2015
dc.identifier.citationEuropean Conference on Mobile Robots (ECMR), 2015es_ES
dc.identifier.isbn978-1-4673-9163-4
dc.identifier.doi10.1109/ECMR.2015.7324187
dc.identifier.urihttp://hdl.handle.net/10433/1659
dc.descriptionThis work is partially funded by the EC-FP7 under grant agreement no. 611153 (TERESA) and the project PAIS-MultiRobot, funded by the Junta de Andalucía (TIC-7390). I. Perez-Hurtado is also supported by the Postdoctoral Junior Grant 2013 co-funded by the Spanish Ministry of Economy and Competitiveness and the Pablo de Olavide University.es_ES
dc.description.abstractRobots 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.es_ES
dc.description.sponsorshipUniversidad Pablo de Olavide. Departamento de Deporte e Informáticaes_ES
dc.format.mimetypeapplication/pdf
dc.language.isoenes_ES
dc.publisherIEEEes_ES
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7324187&tag=1
dc.rightsIEEE
dc.subjectHidden Markov modelses_ES
dc.subjectLearning (artificial intelligence)es_ES
dc.subjectMobile robotses_ES
dc.subjectPath planninges_ES
dc.subjectRobot visiones_ES
dc.titleAn extension of GHMMs for environments with occlusions and automatic goal discovery for person trajectory predictiones_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.description.versionPostprintes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/611153es_ES


Files in this item

This item appears in the following Collection(s)

Show simple item record