Continuous Human Action Recognition in Ambient Assisted Living Scenarios

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dc.contributorInformática Industrial y Redes de Computadoreses
dc.contributorDomótica y Ambientes Inteligenteses
dc.contributor.authorChaaraoui, Alexandros Andre-
dc.contributor.authorFlórez-Revuelta, Francisco-
dc.contributor.otherUniversidad de Alicante. Departamento de Tecnología Informática y Computaciónes
dc.descriptionPaper submitted to the International Workshop on Enhanced Living Environments (ELEMENT-2014), Wuerzburg, Germany, 24 September
dc.description.abstractAmbient assisted living technologies and services make it possible to help elderly and impaired people and increase their personal autonomy. Specifically, vision-based approaches enable the recognition of human behaviour, which in turn allows to build valuable services upon. However, a main constraint is that these have to be able to work online and in real time. In this work, a human action recognition method based on a bag-of-key-poses model and sequence alignment is extended to support continuous human action recognition. The detection of action zones is proposed to locate the most discriminative segments of an action. For the recognition, a method based on a sliding and growing window approach is presented. Furthermore, an evaluation scheme particularly designed for ambient assisted living scenarios is introduced. Experimental results on two publicly available datasets are provided. These show that the proposed action zones lead to a significant improvement and allow real-time
dc.rightsLicencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0es
dc.subjectAmbient assisted livinges
dc.subjectHuman action recognitiones
dc.subjectContinous recognitiones
dc.subjectAction zoneses
dc.subjectReal timees
dc.subject.otherArquitectura y Tecnología de Computadoreses
dc.titleContinuous Human Action Recognition in Ambient Assisted Living Scenarioses
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