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.

本文综述了深度学习方法在视频背景减除中的应用，特别是使用深度神经网络作为前景检测和特征学习的方法，分析了这些方法相对于传统的基于多个特征或多个指标策略的非监督学习方法的性能优势，最后通过在 CDnet 2014 数据集上的实验结果证实了这些深度学习方法的可行性和有效性。