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Background modeling and subtraction is a promising research area with avariety of applications for video surveillance. Recent years have witnessed aproliferation 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 mappingfunctions are learned to approximate the temporal conditional averages ofobserved target backgrounds and foregrounds. On the other hand, statisticallearning in imagery domains has been a prevalent approach with high adaptationto dynamic context transformation, notably using Gaussian Mixture Models (GMM)with its generalization capabilities. By leveraging both, we propose a novelmethod called CDN-MEDAL-net for background modeling and subtraction with twoconvolutional neural networks. The first architecture, CDN-GM, is grounded onan unsupervised GMM statistical learning strategy to describe observed scenes'salient features. The second one, MEDAL-net, implements a light-weightedpipeline of online video background subtraction. Our two-stage architecture issmall, but it is very effective with rapid convergence to representations ofintricate motion patterns. Our experiments show that the proposed approach isnot only capable of effectively extracting regions of moving objects in unseencases, but it is also very efficient.

CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis