Reference : Semantic Background Subtraction
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/213419
Semantic Background Subtraction
English
Braham, Marc mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Pierard, Sébastien mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Van Droogenbroeck, Marc mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Sep-2017
IEEE International Conference on Image Processing (ICIP), Beijing 17-20 September 2017
IEEE
Yes
No
International
IEEE International Conference on Image Processing (ICIP)
17-20 September 2017
Beijing
China
[en] Background subtraction ; Change detection ; Semantic segmentation ; Scene labeling ; Scene parsing ; Classification
[en] We introduce the notion of semantic background subtraction, a novel framework for motion detection in video sequences. The key innovation consists to leverage object-level semantics to address the variety of challenging scenarios for background subtraction. Our framework combines the information of a semantic segmentation algorithm, expressed by a probability for each pixel, with the output of any background subtraction algorithm to reduce false positive detections produced by illumination changes, dynamic backgrounds, strong shadows, and ghosts. In addition, it maintains a fully semantic background model to improve the detection of camouflaged foreground objects. Experiments led on the CDNet dataset show that we managed to improve, significantly, almost all background subtraction algorithms of the CDNet leaderboard, and reduce the mean overall error rate of all the 34 algorithms (resp. of the best 5 algorithms) by roughly 50% (resp. 20%). Note that a C++ implementation of the framework is available at http://www.telecom.ulg.ac.be/semantic.
Department of Electrical Engineering and Computer Science (Montefiore Institute), Signal and Image Exploitation (INTELSIG)
Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/213419
http://www.telecom.ulg.ac.be/semantic
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
Braham2017Semantic.pdfAuthor postprint830.23 kBView/Open

Additional material(s):

File Commentary Size Access
Open access
SemanticBGS-Code.zip79.68 MBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.