# Learning Multi-scale Features for Foreground Segmentation Source: [Learning Multi-scale Features for Foreground Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:45:30.461Z Authors: Long Ang Lim, Hacer Yalim Keles Published: 2018-08-04T12:55:25.000Z ## Abstract Foreground segmentation algorithms aim segmenting moving objects from the background in a robust way under various challenging scenarios. Encoder-decoder type deep neural networks that are used in this domain recently perform impressive segmentation results. In this work, we propose a novel robust encoder-decoder structure neural network that can be trained end-to-end using only a few training examples. The proposed method extends the Feature Pooling Module (FPM) of FgSegNet by introducing features fusions inside this module, which is capable of extracting multi-scale features within images; resulting in a robust feature pooling against camera motion, which can alleviate the need of multi-scale inputs to the network. Our method outperforms all existing state-of-the-art methods in CDnet2014 dataset by an average overall F-Measure of 0.9847. We also evaluate the effectiveness of our method on SBI2015 and UCSD Background Subtraction datasets. The source code of the proposed method is made available at https://github.com/lim-anggun/FgSegNet\_v2 . ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Background Subtraction on CDnet 2014 (full)](): Background Subtraction; CDnet 2014 (full); F1 (baseline): 99.78. ## Original sources and implementation [Original paper]() [PDF]()