SwePub - search: WFRF:(Medina Carnicer R.)

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1.	Munoz-Salinas, Rafael, et al. (author) Multi-camera head pose estimation 2012 In: Machine Vision and Applications. - : Springer. - 0932-8092 .- 1432-1769. ; 23:3, s. 479-490 Journal article (peer-reviewed)abstract 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.

Munoz-Salinas, Rafael, et al. (author)
Multi-camera head pose estimation
2012
In: Machine Vision and Applications. - : Springer. - 0932-8092 .- 1432-1769. ; 23:3, s. 479-490
Journal article (peer-reviewed)abstract
- 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.

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