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Detecting end-effectors on 2.5D data using geometric deformable models: application to human pose estimation
| dc.contributor.author | Suau Cuadros, Xavier |
| dc.contributor.author | Ruiz Hidalgo, Javier |
| dc.contributor.author | Casas Pla, Josep Ramon |
| dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
| dc.date.accessioned | 2014-10-08T17:06:07Z |
| dc.date.created | 2013-03 |
| dc.date.issued | 2013-03 |
| dc.identifier.citation | Suau, X.; Ruiz, J.; Casas, J. Detecting end-effectors on 2.5D data using geometric deformable models: application to human pose estimation. "Computer vision and image understanding", Març 2013, vol. 117, núm. 3, p. 281-288. |
| dc.identifier.issn | 1077-3142 |
| dc.identifier.uri | http://hdl.handle.net/2117/24311 |
| dc.description.abstract | End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out such propagation, implemented according to the Narrow Band Level Set approach. A variant of the latter method is proposed, including a density restriction which helps preserving the topological properties of the object under analysis. Principal end-effectors are extracted from a directed graph weighted with geodesic distances, also providing a skeletal-like structure describing human pose. An evaluation against reference methods is performed with promising results. The proposed solution allows a frame-wise end-effector detection, with no temporal tracking involved, which may be generalized to the tracking of other objects beyond human body. |
| dc.format.extent | 8 p. |
| dc.language.iso | eng |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
| dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| dc.subject.lcsh | Computer vision |
| dc.subject.other | Depth image |
| dc.subject.other | End-effector |
| dc.subject.other | Extremities |
| dc.subject.other | Human pose estimation |
| dc.subject.other | Range camera |
| dc.title | Detecting end-effectors on 2.5D data using geometric deformable models: application to human pose estimation |
| dc.type | Article |
| dc.subject.lemac | Visió per ordinador |
| dc.contributor.group | Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
| dc.identifier.doi | 10.1016/j.cviu.2012.11.006 |
| dc.description.peerreviewed | Peer Reviewed |
| dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S1077314212001907 |
| dc.rights.access | Restricted access - publisher's policy |
| drac.iddocument | 11179701 |
| dc.description.version | Postprint (published version) |
| dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/248138/EU/Format-Agnostic SCript-based INterAcTive Experience/FASCINATE |
| dc.date.lift | 10000-01-01 |
| upcommons.citation.author | Suau, X.; Ruiz, J.; Casas, J. |
| upcommons.citation.published | true |
| upcommons.citation.publicationName | Computer vision and image understanding |
| upcommons.citation.volume | 117 |
| upcommons.citation.number | 3 |
| upcommons.citation.startingPage | 281 |
| upcommons.citation.endingPage | 288 |
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