oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multi-camera head pose estimation
Örebro University, School of Science and Technology.ORCID iD: 0000-0001-8229-1363
2012 (English)In: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 23, no 3, p. 479-490Article in journal (Refereed) Published
Abstract [en]

Estimating people's head pose is an important problem, for which many solutions have been proposed. Most existing solutions are based on the use of a single camera and assume that the head is confined in a relatively small region of space. If we need to estimate unintrusively the head pose of persons in a large environment, however, we need to use several cameras to cover the monitored area. In this work, we propose a novel solution to the multi-camera head pose estimation problem that exploits the additional amount of information that provides multi-camera configurations. Our approach uses the probability estimates produced by multi-class support vector machines to calculate the probability distribution of the head pose. The distributions produced by the cameras are fused, resulting in a more precise estimate than the one provided individually. We report experimental results that confirm that the fused distribution provides higher accuracy than the individual classifiers and a high robustness against errors.

Place, publisher, year, edition, pages
Springer, 2012. Vol. 23, no 3, p. 479-490
Keywords [en]
Head pose, Multiple views, Support vector machines, People tracking
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-22825DOI: 10.1007/s00138-012-0410-zISI: 000302706400006Scopus ID: 2-s2.0-84861703684OAI: oai:DiVA.org:oru-22825DiVA, id: diva2:526569
Available from: 2012-05-14 Created: 2012-05-14 Last updated: 2018-01-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Saffiotti, Alessandro

Search in DiVA

By author/editor
Saffiotti, Alessandro
By organisation
School of Science and Technology
In the same journal
Machine Vision and Applications
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 434 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf