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

Khoshrou, Samaneh et Cardoso, Jaime S. et Granger, Eric et Teixeira, Luis F.. 2015. « Spatio-temporal fusion for learning of regions of interests over multiple video streams ». In Advances in Visual Computing : 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part II (Las Vegas, NV, USA, Dec. 14-16, 2015) Coll. « Lecture Notes in Computer Science », vol. 9475. , p. 509-520. Springer Verlag.

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Résumé

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 GMMUBM (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.

Type de document: Compte rendu de conférence
Professeur:
Professeur
Granger, Éric
Affiliation: Génie de la production automatisée
Date de dépôt: 28 janv. 2016 19:43
Dernière modification: 17 août 2016 19:21
URI: http://espace2.etsmtl.ca/id/eprint/12175

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