  Conventional neural networks show a powerful framework for background
subtraction in video acquired by static cameras. Indeed, the well-known SOBS
method and its variants based on neural networks were the leader methods on the
largescale CDnet 2012 dataset during a long time. Recently, convolutional
neural networks which belong to deep learning methods were employed with
success for background initialization, foreground detection and deep learned
features. Currently, the top current background subtraction methods in CDnet
2014 are based on deep neural networks with a large gap of performance in
comparison on the conventional unsupervised approaches based on multi-features
or multi-cues strategies. Furthermore, a huge amount of papers was published
since 2016 when Braham and Van Droogenbroeck published their first work on CNN
applied to background subtraction providing a regular gain of performance. In
this context, we provide the first review of deep neural network concepts in
background subtraction for novices and experts in order to analyze this success
and to provide further directions. For this, we first surveyed the methods used
background initialization, background subtraction and deep learned features.
Then, we discuss the adequacy of deep neural networks for background
subtraction. Finally, experimental results are presented on the CDnet 2014
dataset.
