Spatio-temporal fusion for learning of regions of interests over multiple video streams

dc.contributor.author Samaneh Khoshrou en
dc.contributor.author Jaime Cardoso en
dc.contributor.author Granger,E en
dc.contributor.author Luís Filipe Teixeira en
dc.date.accessioned 2018-01-12T16:02:41Z
dc.date.available 2018-01-12T16:02:41Z
dc.date.issued 2015 en
dc.description.abstract Video surveillance systems must process and manage a growing amount of data captured over a network of cameras for various recognition tasks. In order to limit human labour and error, this paper presents a spatial-temporal fusion approach to accurately combine information from Region of Interest (RoI) batches captured in a multi-camera surveillance scenario. In this paper, feature-level and score-level approaches are proposed for spatial-temporal fusion of information to combine information over frames, in a framework based on ensembles of GMM-UBM (Universal Background Models). At the feature-level, features in a batch of multiple frames are combined and fed to the ensemble, whereas at the score-level the outcome of ensemble for individual frames are combined. Results indicate that feature-level fusion provides higher level of accuracy in a very efficient way. © Springer International Publishing Switzerland 2015. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/5964
dc.identifier.uri http://dx.doi.org/10.1007/978-3-319-27863-6_47 en
dc.language eng en
dc.relation 5457 en
dc.relation 3889 en
dc.relation 4357 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Spatio-temporal fusion for learning of regions of interests over multiple video streams en
dc.type conferenceObject en
dc.type Publication en
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