  Background modeling and subtraction is a promising research area with a
variety of applications for video surveillance. Recent years have witnessed a
proliferation of effective learning-based deep neural networks in this area.
However, the techniques have only provided limited descriptions of scenes'
properties while requiring heavy computations, as their single-valued mapping
functions are learned to approximate the temporal conditional averages of
observed target backgrounds and foregrounds. On the other hand, statistical
learning in imagery domains has been a prevalent approach with high adaptation
to dynamic context transformation, notably using Gaussian Mixture Models (GMM)
with its generalization capabilities. By leveraging both, we propose a novel
method called CDN-MEDAL-net for background modeling and subtraction with two
convolutional neural networks. The first architecture, CDN-GM, is grounded on
an unsupervised GMM statistical learning strategy to describe observed scenes'
salient features. The second one, MEDAL-net, implements a light-weighted
pipeline of online video background subtraction. Our two-stage architecture is
small, but it is very effective with rapid convergence to representations of
intricate motion patterns. Our experiments show that the proposed approach is
not only capable of effectively extracting regions of moving objects in unseen
cases, but it is also very efficient.
