# Towards Benchmarking Scene Background Initialization

arXiv: https://arxiv.org/abs/1506.04051 · HTML: https://mlsanity.com/paper/1506.04051/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2015.

## Built on by (most cited first, 13 of 13)

- [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)
- [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)
- [Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee](https://mlsanity.com/paper/2203.16328/) (2022)
- [Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation](https://mlsanity.com/paper/2304.09949/) (2023)
- [Quaternion-based dynamic mode decomposition for background modeling in color videos](https://mlsanity.com/paper/2112.13982/) (2021)
- [Spatio-temporal Data Augmentation for Visual Surveillance](https://mlsanity.com/paper/2101.09895/) (2021)
- [DeePaste -- Inpainting for Pasting](https://mlsanity.com/paper/2112.10600/) (2021)
- [FADE: A Dataset for Detecting Falling Objects around Buildings in Video](https://mlsanity.com/paper/2408.05750/) (2024)
- [Tensor Robust PCA with Nonconvex and Nonlocal Regularization](https://mlsanity.com/paper/2211.02404/) (2022)
- [MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation](https://mlsanity.com/paper/2402.00918/) (2024)
- [Quaternion-Based Robust PCA for Efficient Moving Target Detection and Background Recovery in Color Videos](https://mlsanity.com/paper/2507.19730/) (2025)
- [Event Detection in Videos: A Framework for the Development of New Methods](https://mlsanity.com/paper/2607.04372/) (2026)

## Abstract (arXiv)

Given a set of images of a scene taken at different times, the availability of an initial background model that describes the scene without foreground objects is the prerequisite for a wide range of applications, ranging from video surveillance to computational photography. Even though several methods have been proposed for scene background initialization, the lack of a common groundtruthed dataset and of a common set of metrics makes it difficult to compare their performance. To move first steps towards an easy and fair comparison of these methods, we assembled a dataset of sequences frequently adopted for background initialization, selected or created ground truths for quantitative evaluation through a selected suite of metrics, and compared results obtained by some existing methods, making all the material publicly available.