# Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation

arXiv: https://arxiv.org/abs/1811.05255 · HTML: https://mlsanity.com/paper/1811.05255/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

## Built on by (most cited first, 26 of 26)

- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [A Study of the Generalizability of Self-Supervised Representations](https://mlsanity.com/paper/2109.09150/) (2021)
- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (2021)
- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [Universal Background Subtraction based on Arithmetic Distribution Neural Network](https://mlsanity.com/paper/2104.08390/) (2021)
- [An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs](https://mlsanity.com/paper/2105.01342/) (2021)
- [ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection](https://mlsanity.com/paper/2303.14679/) (2023)
- [Optical flow-based branch segmentation for complex orchard environments](https://mlsanity.com/paper/2202.13050/) (2022)
- [Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos](https://mlsanity.com/paper/2207.06440/) (2022)
- [Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation](https://mlsanity.com/paper/2304.09949/) (2023)
- [Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction](https://mlsanity.com/paper/2309.15576/) (2023)
- [Quaternion-based dynamic mode decomposition for background modeling in color videos](https://mlsanity.com/paper/2112.13982/) (2021)
- [Dynamic Background Subtraction by Generative Neural Networks](https://mlsanity.com/paper/2202.05336/) (2022)
- [Inductive Graph Neural Networks for Moving Object Segmentation](https://mlsanity.com/paper/2305.09585/) (2023)
- [The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory](https://mlsanity.com/paper/2306.14753/) (2023)
- [Towards Automatic Model Specialization for Edge Video Analytics](https://mlsanity.com/paper/2104.06826/) (2021)
- [An exploration of the performances achievable by combining unsupervised background subtraction algorithms](https://mlsanity.com/paper/2202.12563/) (2022)
- [Working memory inspired hierarchical video decomposition with transformative representations](https://mlsanity.com/paper/2204.10105/) (2022)
- [How to Track and Segment Fish without Human Annotations: A Self-Supervised Deep Learning Approach](https://mlsanity.com/paper/2208.10662/) (2022)
- [Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling](https://mlsanity.com/paper/2307.04159/) (2023)
- [FADE: A Dataset for Detecting Falling Objects around Buildings in Video](https://mlsanity.com/paper/2408.05750/) (2024)
- [A Methodology to Evaluate Strategies Predicting Rankings on Unseen Domains](https://mlsanity.com/paper/2505.15595/) (2025)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis](https://mlsanity.com/paper/2106.03776/) (2021)
- [Weakly Supervised Realtime Dynamic Background Subtraction](https://mlsanity.com/paper/2303.02857/) (2023)
- [MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation](https://mlsanity.com/paper/2402.00918/) (2024)

## Abstract (arXiv)

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.