Sökning: onr:"swepub:oai:DiVA.org:oru-22825" >
Multi-camera head p...
Multi-camera head pose estimation
-
Munoz-Salinas, Rafael (författare)
-
Yeguas-Bolivar, E. (författare)
-
- Saffiotti, Alessandro (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik
-
visa fler...
-
Medina-Carnicer, R. (författare)
-
visa färre...
-
(creator_code:org_t)
- 2012-02-21
- 2012
- Engelska.
-
Ingår i: Machine Vision and Applications. - : Springer. - 0932-8092 .- 1432-1769. ; 23:3, s. 479-490
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Head pose
- Multiple views
- Support vector machines
- People tracking
- Computer and Systems Science
- Datalogi
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas