# Dynamic Background Subtraction by Generative Neural Networks

arXiv: https://arxiv.org/abs/2202.05336 · HTML: https://mlsanity.com/paper/2202.05336/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 60 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Background Subtraction in Real Applications: Challenges, Current Models and Future Directions](https://mlsanity.com/paper/1901.03577/) (2019)
- [Learning Multi-scale Features for Foreground Segmentation](https://mlsanity.com/paper/1808.01477/) (2018)
- [Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation](https://mlsanity.com/paper/1811.05255/) (2018)
- [BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos](https://mlsanity.com/paper/1907.11371/) (2019)
- [Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding](https://mlsanity.com/paper/1801.02225/) (2018)
- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (2021)
- [WisenetMD: Motion Detection Using Dynamic Background Region Analysis](https://mlsanity.com/paper/1805.09277/) (2018)
- [How Far Can You Get By Combining Change Detection Algorithms?](https://mlsanity.com/paper/1505.02921/) (2015)
- [Real-Time Semantic Background Subtraction](https://mlsanity.com/paper/2002.04993/) (2020)
- [Online Illumination Invariant Moving Object Detection by Generative Neural Network](https://mlsanity.com/paper/1808.01066/) (2018)
- [CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction](https://mlsanity.com/paper/1804.07008/) (2018)
- [M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance](https://mlsanity.com/paper/1802.04979/) (2018)
- [CVABS: Moving Object Segmentation with Common Vector Approach for Videos](https://mlsanity.com/paper/1810.08412/) (2018)

## Built on by (most cited first, 3 of 3)

- [ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection](https://mlsanity.com/paper/2303.14679/) (2023)
- [Weakly Supervised Realtime Dynamic Background Subtraction](https://mlsanity.com/paper/2303.02857/) (2023)
- [Decomposer: Semi-supervised Learning of Image Restoration and Image Decomposition](https://mlsanity.com/paper/2311.16829/) (2023)

## Abstract (arXiv)

Background subtraction is a significant task in computer vision and an essential step for many real world applications. One of the challenges for background subtraction methods is dynamic background, which constitute stochastic movements in some parts of the background. In this paper, we have proposed a new background subtraction method, called DBSGen, which uses two generative neural networks, one for dynamic motion removal and another for background generation. At the end, the foreground moving objects are obtained by a pixel-wise distance threshold based on a dynamic entropy map. The proposed method has a unified framework that can be optimized in an end-to-end and unsupervised fashion. The performance of the method is evaluated over dynamic background sequences and it outperforms most of state-of-the-art methods. Our code is publicly available at https://github.com/FatemeBahri/DBSGen.