Deep Background Subtraction with Scene-Specific Convolutional Neural Networks

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Keywords :

Background subtraction; CDnet; Change detection; Convolutional neural networks; Deep learning; Surveillance; Machine learning

Abstract :

[en] Background subtraction is usually based on low-level or hand-crafted features such as raw color components, gradients, or local binary patterns. As an improvement, we present a background subtraction algorithm based on spatial features learned with convolutional neural networks (ConvNets). Our algorithm uses a background model reduced to a single background image and a scene-specific training dataset to feed ConvNets that prove able to learn how to subtract the background from an input image patch. Experiments led on 2014 ChangeDetection.net dataset show that our ConvNet based algorithm at least reproduces the performance of state-of-the-art methods, and that it even outperforms them significantly when scene-specific knowledge is considered.

Research center :

Department of Electrical Engineering and Computer Science (Montefiore Institute), Signal and Image Exploitation (INTELSIG)

Disciplines :

Computer science

Author, co-author :

Braham, Marc ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications

Van Droogenbroeck, Marc ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications

Language :

English

Title :

Deep Background Subtraction with Scene-Specific Convolutional Neural Networks

Publication date :

May 2016

Event name :

IEEE International Conference on Systems, Signals and Image Processing (IWSSIP)

Event place :

Bratislava, Slovakia

Event date :

23-25 May 2016

Audience :

International

Main work title :

IEEE International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava 23-25 May 2016

Publisher :

IEEE

Pages :

1-4

Peer reviewed :

Peer reviewed

Additional URL :

http://www.telecom.ulg.ac.be/publi/publications/braham/Braham2016Deep/index.html
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7502717

Funders :

FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture [BE]

Available on ORBi :

since 01 April 2016

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Scopus citations^®

257

Scopus citations^®
without self-citations

253

OpenCitations

146

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