Skin detection by dual maximization of detectors agreement for video monitoring
EntidadUAM. Departamento de Tecnología Electrónica y de las Comunicaciones
Fecha de edición2013-12
10.1016/j.patrec.2013.07.016Pattern Recognition Letters, 34.16 (2013): 2102-2109
ISSN0167-8655 (print); 1872-7344 (online)
Financiado porThis work has been partially supported by the Spanish Government (TEC2011-25995 EventVideo).
Versión del editorhttp://dx.doi.org/10.1016/j.patrec.2013.07.016
MateriasColor space selection; Detector adaptation; Performance optimization; Skin detection; Informática; Telecomunicaciones
NotaThis is the author’s version of a work that was accepted for publication in Journal Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Pattern Recognition Letters, 34, 16 (2013) DOI: 10.1016/j.patrec.2013.07.016
Derechos© 2013 Elsevier B.V. All rights reserved
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
This paper presents an approach for skin detection which is able to adapt its parameters to image data captured from video monitoring tasks with a medium field of view. It is composed of two detectors designed to get high and low probable skin pixels (respectively, regions and isolated pixels). Each one is based on thresholding two color channels, which are dynamically selected. Adaptation is based on the agreement maximization framework, whose aim is to find the configuration with the highest similarity between the channel results. Moreover, we improve such framework by learning how detector parameters are related and proposing an agreement function to consider expected skin properties. Finally, both detectors are combined by morphological reconstruction filtering to keep the skin regions whilst removing wrongly detected regions. The proposed approach is evaluated on heterogeneous human activity recognition datasets outperforming the most relevant state-of-the-art approaches.
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