# Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

arXiv: https://arxiv.org/abs/1901.03577 · HTML: https://mlsanity.com/paper/1901.03577/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

## Built on by (most cited first, 29 of 29)

- [NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation](https://mlsanity.com/paper/2203.04802/) (2022)
- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (2021)
- [Universal Background Subtraction based on Arithmetic Distribution Neural Network](https://mlsanity.com/paper/2104.08390/) (2021)
- [Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection](https://mlsanity.com/paper/2403.01968/) (2024)
- [ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection](https://mlsanity.com/paper/2303.14679/) (2023)
- [Autoencoder-based background reconstruction and foreground segmentation with background noise estimation](https://mlsanity.com/paper/2112.08001/) (2021)
- [Unsupervised Anomaly Detection from Time-of-Flight Depth Images](https://mlsanity.com/paper/2203.01052/) (2022)
- [Instance-Level Moving Object Segmentation from a Single Image with Events](https://mlsanity.com/paper/2502.12975/) (2025)
- [Deep Learning for Robust Motion Segmentation with Non-Static Cameras](https://mlsanity.com/paper/2102.10929/) (2021)
- [Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee](https://mlsanity.com/paper/2203.16328/) (2022)
- [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 Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction](https://mlsanity.com/paper/2309.15576/) (2023)
- [Traffic Video Object Detection using Motion Prior](https://mlsanity.com/paper/2311.10092/) (2023)
- [Nondestructive Testing of Composite Fibre Materials with Hyperspectral Imaging : Evaluative Studies in the EU H2020 FibreEUse Project](https://mlsanity.com/paper/2111.03443/) (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)
- [HSMD: An object motion detection algorithm using a Hybrid Spiking Neural Network Architecture](https://mlsanity.com/paper/2109.04119/) (2021)
- [rSVDdpd: A Robust Scalable Video Surveillance Background Modelling Algorithm](https://mlsanity.com/paper/2109.10680/) (2021)
- [An exploration of the performances achievable by combining unsupervised background subtraction algorithms](https://mlsanity.com/paper/2202.12563/) (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)
- [A quantum moving target segmentation algorithm for grayscale video](https://mlsanity.com/paper/2310.03038/) (2023)
- [CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis](https://mlsanity.com/paper/2106.03776/) (2021)
- [NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector](https://mlsanity.com/paper/2112.06102/) (2021)
- [Spatio-Temporal Feedback Control of Small Target Motion Detection Visual System](https://mlsanity.com/paper/2211.10128/) (2022)
- [Weakly Supervised Realtime Dynamic Background Subtraction](https://mlsanity.com/paper/2303.02857/) (2023)
- [Discovering Local Binary Pattern Equation for Foreground Object Removal in Videos](https://mlsanity.com/paper/2308.06305/) (2023)
- [TSOM: Small Object Motion Detection Neural Network Inspired by Avian Visual Circuit](https://mlsanity.com/paper/2404.00855/) (2024)

## Abstract (arXiv)

Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.