CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis
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by
Nguyen-Tien Cuong, Hung Ngoc Phan, Nhat Minh Chung, Phuong Hoai Ha, Synh Viet-Uyen Ha
2021
Abstract
Background modeling is a promising research area in video analysis with a
variety of video surveillance applications. Recent years have witnessed the
proliferation of deep neural networks via effective learning-based approaches
in motion analysis. However, these techniques only provide a limited
description of the observed scenes' insufficient properties where a
single-valued mapping is learned to approximate the temporal conditional
averages of the target background. On the other hand, statistical learning in
imagery domains has become one of the most prevalent approaches with high
adaptation to dynamic context transformation, notably Gaussian Mixture Models,
combined with a foreground extraction step. In this work, we propose a novel,
two-stage method of change detection with two convolutional neural networks.
The first architecture is grounded on the unsupervised Gaussian mixtures
statistical learning to describe the scenes' salient features. The second one
implements a light-weight pipeline of foreground detection. Our two-stage
framework contains approximately 3.5K parameters in total but still maintains
rapid convergence to intricate motion patterns. Our experiments on publicly
available datasets show that our proposed networks are not only capable of
generalizing regions of moving objects in unseen cases with promising results
but also are competitive in performance efficiency and effectiveness regarding
foreground segmentation.
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