dc.creatorGLORIA CASTRO MUNOZ
dc.date2015-12-11
dc.date.accessioned2018-11-19T14:24:57Z
dc.date.available2018-11-19T14:24:57Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/27
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/2258177
dc.descriptionThe Human Action Recognition (HAR) from video sequences is a topic which has captured the interest of a large number of researchers from industry, academia, consumer agencies and security agencies. The solid interest in the topic is motivated by the wide variety and importance of promising applications for example rehabilitation of patients, monitoring and supporting of children and elderly people, automatic annotation of video, human-computer interfaces, and video surveillance among others. Particularly the increasing demand for security and safety by society in recent years has occasioned significant advances in video surveillance technology. However despite these advances, video surveillance systems are not able to analyze in realtime the huge amounts of video coming from video surveillance cameras installed in the worldwide and therefore, they can’t detect and alert about potential criminal activity in real-time.
dc.formatapplication/pdf
dc.languageeng
dc.publisherInstituto Nacional de Astrofísica, Óptica y Electrónica
dc.relationcitation:Castro-Muñoz G.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/Tiempo real/Real-time
dc.subjectinfo:eu-repo/classification/Reconocimiento/Human action recognition
dc.subjectinfo:eu-repo/classification/Video/Video surveillance
dc.subjectinfo:eu-repo/classification/SVM/Support Vector Machine
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/22
dc.subjectinfo:eu-repo/classification/cti/2203
dc.titleReal-time human action recognition using a reduced feature set
dc.typeTesis
dc.audiencegeneralPublic


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