# Combining Background Subtraction Algorithms with Convolutional Neural Network

arXiv: https://arxiv.org/abs/1807.02080 · HTML: https://mlsanity.com/paper/1807.02080/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [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)
- [Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation](https://mlsanity.com/paper/2304.09949/) (2023)
- [An exploration of the performances achievable by combining unsupervised background subtraction algorithms](https://mlsanity.com/paper/2202.12563/) (2022)
- [Large-Scale Video Analytics through Object-Level Consolidation](https://mlsanity.com/paper/2111.15451/) (2021)
- [NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector](https://mlsanity.com/paper/2112.06102/) (2021)
- [MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation](https://mlsanity.com/paper/2402.00918/) (2024)

## Abstract (arXiv)

Accurate and fast extraction of foreground object is a key prerequisite for a wide range of computer vision applications such as object tracking and recognition. Thus, enormous background subtraction methods for foreground object detection have been proposed in recent decades. However, it is still regarded as a tough problem due to a variety of challenges such as illumination variations, camera jitter, dynamic backgrounds, shadows, and so on. Currently, there is no single method that can handle all the challenges in a robust way. In this letter, we try to solve this problem from a new perspective by combining different state-of-the-art background subtraction algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder-decoder fully convolutional neural network architecture is trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different background subtraction algorithms and output a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single background subtraction algorithm. And we show that our solution is more efficient than other combination strategies.