Optimizing human action recognition based on a cooperative coevolutionary algorithm

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Title: Optimizing human action recognition based on a cooperative coevolutionary algorithm
Authors: Chaaraoui, Alexandros Andre | Flórez-Revuelta, Francisco
Research Group/s: Informática Industrial y Redes de Computadores | Domótica y Ambientes Inteligentes
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Human action recognition | Evolutionary computation | Instance selection | Feature subset selection | Coevolution
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: 30-Oct-2013
Publisher: Elsevier
Citation: Chaaraoui, A.A., Flórez-Revuelta, F., Optimizing human action recognition based on a cooperative coevolutionary algorithm. Eng. Appl. Artif. Intel. (2013), http://dx.doi.org/10.1016/j.engappai.2013.10.003i
Abstract: Vision-based human action recognition is an essential part of human behavior analysis, which is currently in great demand due to its wide area of possible applications. In this paper, an optimization of a human action recognition method based on a cooperative coevolutionary algorithm is proposed. By means of coevolution, three different populations are evolved to obtain the best performing individuals with respect to instance, feature and parameter selection. The fitness function is based on the result of the human action recognition method. Using a multi-view silhouette-based pose representation and a weighted feature fusion scheme, an efficient feature is obtained, which takes into account the multiple views and their relevance. Classification is performed by means of a bag of key poses, which represents the most characteristic pose representations, and matching of sequences of key poses. The performed experimentation indicates that not only a considerable performance gain is obtained outperforming the success rates of other state-of-the-art methods, but also the temporal and spatial performance of the algorithm is improved.
Sponsor: 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/10045/33676
ISSN: 0952-1976 (Print) | 1873-6769 (Online)
DOI: 10.1016/j.engappai.2013.10.003i
Language: eng
Type: info:eu-repo/semantics/article
Peer Review: si
Publisher version: http://dx.doi.org/10.1016/j.engappai.2013.10.003i
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