Human action recognition optimization based on evolutionary feature subset selection

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Título: Human action recognition optimization based on evolutionary feature subset selection
Autor/es: Chaaraoui, Alexandros Andre | Flórez-Revuelta, Francisco
Grupo/s de investigación o GITE: Informática Industrial y Redes de Computadores | Domótica y Ambientes Inteligentes
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Palabras clave: Feature subset selection | Genetic algorithm | Human action recognition | Multi-view recognition | Bag-of-key-poses
Área/s de conocimiento: Arquitectura y Tecnología de Computadores
Fecha de publicación: jul-2013
Editor: ACM
Cita bibliográfica: Chaaraoui, Alexandros Andre; Flórez-Revuelta, Francisco. “Human action recognition optimization based on evolutionary feature subset selection”. En: GECCO '13 Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference. New York : ACM, 2013. ISBN 978-1-4503-1963-8, pp. 1229-1236
Resumen: Human action recognition constitutes a core component of advanced human behavior analysis. The detection and recognition of basic human motion enables to analyze and understand human activities, and to react proactively providing different kinds of services from human-computer interaction to health care assistance. In this paper, a feature-level optimization for human action recognition is proposed. The resulting recognition rate and computational cost are significantly improved by means of a low-dimensional radial summary feature and evolutionary feature subset selection. The introduced feature is computed using only the contour points of human silhouettes. These are spatially aligned based on a radial scheme. This definition shows to be proficient for feature subset selection, since different parts of the human body can be selected by choosing the appropriate feature elements. The best selection is sought using a genetic algorithm. Experimentation has been performed on the publicly available MuHAVi dataset. Promising results are shown, since state-of-the-art recognition rates are considerably outperformed with a highly reduced computational cost.
Patrocinador/es: This work has been partially supported by the European Commission under project “caring4U - A study on people activity in private spaces: towards a multisensor network that meets privacy requirements” (PIEF-GA-2010-274649) and by the Spanish Ministry of Science and Innovation under project “Sistema de visión para la monitorización de la actividad de la vida diaria en el hogar” (TIN2010-20510-C04-02). Alexandros Andre Chaaraoui acknowledges financial support by the Conselleria d'Educació, Formació i Ocupació of the Generalitat Valenciana (fellowship ACIF/20-11/160).
URI: http://hdl.handle.net/10045/33675
ISBN: 978-1-4503-1963-8
DOI: 10.1145/2463372.2463529
Idioma: eng
Tipo: info:eu-repo/semantics/conferenceObject
Derechos: © 2013 ACM, Inc.
Revisión científica: si
Versión del editor: http://dx.doi.org/10.1145/2463372.2463529
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