[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/Unit :
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
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