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