  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 .
