  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.
