Adaptive Human Action Recognition With an Evolving Bag of Key Poses
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Título: | Adaptive Human Action Recognition With an Evolving Bag of Key Poses |
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Autor/es: | Chaaraoui, Alexandros Andre | Flórez-Revuelta, Francisco |
Grupo/s de investigación o GITE: | Informática Industrial y Redes de Computadores |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Evolutionary computing and genetic algorithms | Feature evaluation and selection | Human computer interaction | Vision and Scene Understanding |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 7-abr-2014 |
Editor: | IEEE |
Cita bibliográfica: | IEEE Transactions on Autonomous Mental Development. 2014. doi:10.1109/TAMD.2014.2315676 |
Resumen: | Vision-based human action recognition allows to detect and understand meaningful human motion. This makes it possible to perform advanced human-computer interaction, among other applications. In dynamic environments, adaptive methods are required to support changing scenario characteristics. Specifically, in human-robot interaction, smooth interaction between humans and robots can only be performed if these are able to evolve and adapt to the changing nature of the scenarios. In this paper, an adaptive vision-based human action recognition method is proposed. By means of an evolutionary optimisation method, adaptive and incremental learning of human actions is supported. Through an evolving bag of key poses, which models the learnt actions over time, the current learning memory is developed to recognise increasingly more actions or actors. The evolutionary method selects the optimal subset of training instances, features and parameter values for each learning phase, and handles the evolution of the model. The experimentation shows that our proposal achieves to adapt to new actions or actors successfully, by rearranging the learnt model. Stable and accurate results have been obtained on four publicly available RGB and RGB-D datasets, unveiling the method’s robustness and applicability. |
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/2011/160). |
URI: | http://hdl.handle.net/10045/37068 |
ISSN: | 1943-0604 (Print) | 1943-0612 (Online) |
DOI: | 10.1109/TAMD.2014.2315676 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © Copyright 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1109/TAMD.2014.2315676 |
Aparece en las colecciones: | INV - I2RC - Artículos de Revistas INV - AmI4AHA - Artículos de Revistas |
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Archivo | Descripción | Tamaño | Formato | |
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2014_Chaaraoui_Florez_IEEE-TAMD.pdf | Versión revisada (acceso abierto) | 7,41 MB | Adobe PDF | Abrir Vista previa |
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