Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding
This work addresses robust moving object segmentation for video analysis applications, representing an incremental improvement over existing methods.
The paper tackles foreground segmentation in videos under challenging conditions like illumination changes and camera motion by proposing a triplet convolutional neural network for multiscale feature encoding, achieving state-of-the-art performance with an average F-Measure of 0.9770 on the Change Detection 2014 Challenge.
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.