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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Muñoz-Salinas, R., Yeguas-Bolivar, E., Saffiotti, A. et al. Multi-camera head pose estimation. Machine Vision and Applications 23, 479–490 (2012). https://doi.org/10.1007/s00138-012-0410-z
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DOI: https://doi.org/10.1007/s00138-012-0410-z