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Real time people detection combining appearance and depth image spaces using boosted random ferns
dc.contributor.author | Vaquero Gómez, Víctor |
dc.contributor.author | Villamizar Vergel, Michael Alejandro |
dc.contributor.author | Sanfeliu Cortés, Alberto |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2016-02-25T13:46:23Z |
dc.date.available | 2016-02-25T13:46:23Z |
dc.date.issued | 2015 |
dc.identifier.citation | Vaquero, V., Villamizar, M.A., Sanfeliu, A. Real time people detection combining appearance and depth image spaces using boosted random ferns. A: Iberian Robotics Conference. "Robot 2015: Second Iberian Robotics Conference". Lisboa: Springer, 2015, p. 587-598. |
dc.identifier.isbn | 978-3-319-27149-1 |
dc.identifier.uri | http://hdl.handle.net/2117/83442 |
dc.description.abstract | This paper presents a robust and real-time method for people detection in urban and crowed environments. Unlike other conventional methods which either focus on single features or compute multiple and independent classifiers specialized in a particular feature space, the proposed approach creates a synergic combination of appearance and depth cues in a unique classifier. The core of our method is a Boosted Random Ferns classifier that selects automatically the most discriminative local binary features for both the appearance and depth image spaces. Based on this classifier, a fast and robust people detector which maintains high detection rates in spite of environmental changes is created. The proposed method has been validated in a challenging RGB-D database of people in urban scenarios and has shown that outperforms state-of-the-art approaches in spite of the difficult environment conditions. As a result, this method is of special interest for real-time robotic applications where people detection is a key matter, such as human-robot interaction or safe navigation of mobile robots for example. |
dc.format.extent | 12 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject.other | feature extraction |
dc.subject.other | object detection |
dc.title | Real time people detection combining appearance and depth image spaces using boosted random ferns |
dc.type | Conference report |
dc.contributor.group | Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents |
dc.identifier.doi | 10.1007/978-3-319-27149-1_45 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.inspec | Classificació INSPEC::Cybernetics::Artificial intelligence::Learning (artificial intelligence) |
dc.relation.publisherversion | http://link.springer.com/chapter/10.1007/978-3-319-27149-1_45 |
dc.rights.access | Open Access |
local.identifier.drac | 17529695 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/605598/EU/Cargo handling by Automated Next generation Transportation Systems for ports and terminals/CARGO-ANTS |
local.citation.author | Vaquero, V.; Villamizar, M.A.; Sanfeliu, A. |
local.citation.contributor | Iberian Robotics Conference |
local.citation.pubplace | Lisboa |
local.citation.publicationName | Robot 2015: Second Iberian Robotics Conference |
local.citation.startingPage | 587 |
local.citation.endingPage | 598 |