# Real-Time Semantic Background Subtraction

arXiv: https://arxiv.org/abs/2002.04993 · HTML: https://mlsanity.com/paper/2002.04993/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

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

- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (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)
- [Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling](https://mlsanity.com/paper/2307.04159/) (2023)
- [A Methodology to Evaluate Strategies Predicting Rankings on Unseen Domains](https://mlsanity.com/paper/2505.15595/) (2025)
- [Weakly Supervised Realtime Dynamic Background Subtraction](https://mlsanity.com/paper/2303.02857/) (2023)

## Abstract (arXiv)

Semantic background subtraction SBS has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a semantic segmentation network. However, SBS requires high-quality semantic segmentation masks for all frames, which are slow to compute. In addition, most state-of-the-art background subtraction algorithms are not real-time, which makes them unsuitable for real-world applications. In this paper, we present a novel background subtraction algorithm called Real-Time Semantic Background Subtraction (denoted RT-SBS) which extends SBS for real-time constrained applications while keeping similar performances. RT-SBS effectively combines a real-time background subtraction algorithm with high-quality semantic information which can be provided at a slower pace, independently for each pixel. We show that RT-SBS coupled with ViBe sets a new state of the art for real-time background subtraction algorithms and even competes with the non real-time state-of-the-art ones. Note that we provide python CPU and GPU implementations of RT-SBS at https://github.com/cioppaanthony/rt-sbs.