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dc.contributor.authorİkizler, Nazlıen_US
dc.contributor.authorDuygulu, Pınaren_US
dc.coverage.spatialRio de Janeiro, Brazil
dc.date.accessioned2016-02-08T11:41:03Z
dc.date.available2016-02-08T11:41:03Z
dc.date.issued2007-10en_US
dc.identifier.urihttp://hdl.handle.net/11693/26976
dc.descriptionDate of Conference: 20 October, 2007
dc.descriptionConference name: Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation, 2007
dc.description.abstractWe describe a "bag-of-rectangles" method for representing and recognizing human actions in videos. In this method, each human pose in an action sequence is represented by oriented rectangular patches extracted over the whole body. Then, spatial oriented histograms are formed to represent the distribution of these rectangular patches. In order to carry the information from the spatial domain described by the bag-of-rectangles descriptor to temporal domain for recognition of the actions, four different methods are proposed. These are namely, (i) frame by frame voting, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis by rectangular patches, (iii) a classifier based approach using SVMs, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the descriptor. The detailed experiments are carried out on the action dataset of Blank et. al. High success rates (100%) prove that with a very simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations. © Springer-Verlag Berlin Heidelberg 2007.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings of the 2nd conference on Human motion: understanding, modeling, capture and animatio, 2007en_US
dc.relation.isversionofhttps://link.springer.com/chapter/10.1007/978-3-540-75703-0_19
dc.subjectData reductionen_US
dc.subjectData structuresen_US
dc.subjectImage analysisen_US
dc.subjectImage reconstructionen_US
dc.subjectSupport vector machinesen_US
dc.subjectRectangular patchesen_US
dc.subjectSpatial domainsen_US
dc.subjectSpatial oriented histogramsen_US
dc.subjectGesture recognitionen_US
dc.titleHuman action recognition using distribution of oriented rectangular patchesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage271en_US
dc.citation.epage284en_US
dc.publisherSpringer


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