# Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding

arXiv: https://arxiv.org/abs/1801.02225 · HTML: https://mlsanity.com/paper/1801.02225/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2018.

## Built on by (most cited first, 16 of 16)

- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (2021)
- [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)
- [Instance-Level Moving Object Segmentation from a Single Image with Events](https://mlsanity.com/paper/2502.12975/) (2025)
- [LangGas: Introducing Language in Selective Zero-Shot Background Subtraction for Semi-Transparent Gas Leak Detection with a New Dataset](https://mlsanity.com/paper/2503.02910/) (2025)
- [Deep Learning for Robust Motion Segmentation with Non-Static Cameras](https://mlsanity.com/paper/2102.10929/) (2021)
- [Dynamic Background Subtraction by Generative Neural Networks](https://mlsanity.com/paper/2202.05336/) (2022)
- [SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtraction](https://mlsanity.com/paper/2505.07336/) (2025)
- [Spatio-temporal Data Augmentation for Visual Surveillance](https://mlsanity.com/paper/2101.09895/) (2021)
- [Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling](https://mlsanity.com/paper/2307.04159/) (2023)
- [CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis](https://mlsanity.com/paper/2106.03776/) (2021)
- [Saliency detection with moving camera via background model completion](https://mlsanity.com/paper/2111.01681/) (2021)
- [Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks](https://mlsanity.com/paper/2212.10417/) (2022)
- [Video Segmentation Learning Using Cascade Residual Convolutional Neural Network](https://mlsanity.com/paper/2212.10570/) (2022)
- [Weakly Supervised Realtime Dynamic Background Subtraction](https://mlsanity.com/paper/2303.02857/) (2023)
- [DIMOS: Disentangling Instance-level Moving Object Segmentation](https://mlsanity.com/paper/2606.12826/) (2026)

## Abstract (arXiv)

A common approach for moving objects segmentation in a scene is to perform a background subtraction. Several methods have been proposed in this domain. However, they lack the ability of handling various difficult scenarios such as illumination changes, background or camera motion, camouflage effect, shadow etc. To address these issues, we propose a robust and flexible encoder-decoder type neural network based approach. We adapt a pre-trained convolutional network, i.e. VGG-16 Net, under a triplet framework in the encoder part to embed an image in multiple scales into the feature space and use a transposed convolutional network in the decoder part to learn a mapping from feature space to image space. We train this network end-to-end by using only a few training samples. Our network takes an RGB image in three different scales and produces a foreground segmentation probability mask for the corresponding image. In order to evaluate our model, we entered the Change Detection 2014 Challenge (changedetection.net) and our method outperformed all the existing state-of-the-art methods by an average F-Measure of 0.9770. Our source code will be made publicly available at https://github.com/lim-anggun/FgSegNet.