# Illumination-Based Data Augmentation for Robust Background Subtraction

arXiv: https://arxiv.org/abs/1910.08470 · HTML: https://mlsanity.com/paper/1910.08470/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval](https://mlsanity.com/paper/2208.02080/) (2022)
- [Domain Generalization for 6D Pose Estimation Through NeRF-based Image Synthesis](https://mlsanity.com/paper/2407.10762/) (2024)
- [Random Shadows and Highlights: A new data augmentation method for extreme lighting conditions](https://mlsanity.com/paper/2101.05361/) (2021)
- [Domain Generalization for In-Orbit 6D Pose Estimation](https://mlsanity.com/paper/2406.11743/) (2024)
- [An experimental evaluation of Siamese Neural Networks for robot localization using omnidirectional imaging in indoor environments](https://mlsanity.com/paper/2407.10536/) (2024)

## Abstract (arXiv)

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In this paper, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly, but also features semantic transformations of illumination which enhance the generalisation of the model. It successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask that is randomly generated. Such data allows us to effectively train an illumination-invariant deep learning model for BGS. Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation.