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dc.contributor.authorMedina-Carnicer, R.
dc.contributor.authorCastro, F.M.
dc.contributor.authorMarín-Pérez, M.J.
dc.date.accessioned2017-01-17T12:32:33Z
dc.date.available2017-01-17T12:32:33Z
dc.date.issued2017-01-17
dc.identifier.urihttp://hdl.handle.net/10396/14309
dc.descriptionSubmitted to International Conference on Pattern Recognition, ICPR, 2014es_ES
dc.description.abstractThe goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person. Thus, obtaining a pyramidal representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on the recent `AVA Multiview Gait' dataset. The results show that this new approach achieves promising results in the problem of gait recognition.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/es/es_ES
dc.source22nd International Conference on Pattern Recognition (ICPR), 2014es_ES
dc.subjectComputer visiones_ES
dc.subjectPattern recognitiones_ES
dc.subjectGait recognition
dc.titlePyramidal Fisher Motion for Multiview Gait Recognitiones_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1109/ICPR.2014.298
dc.relation.projectIDGobierno de España. TIN2012-32952
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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