GraphBGS: Background Subtraction via Recovery of Graph Signals
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Graph-based algorithms have been successful approaching the problems of unsupervised and semi-supervised learning. Recently, the theory of graph signal processing and semi-supervised learning have been combined leading to new developments and insights in the field of machine learning. In this paper, concepts of recovery of graph signals and semi-supervised learning are introduced in the problem of background subtraction. We propose a new algorithm named GraphBGS, this method uses a Mask R-CNN for instances segmentation; temporal median filter for background initialization; motion, texture, color, and structural features for representing the nodes of a graph; k-nearest neighbors for the construction of the graph; and finally a semi-supervised method inspired from the theory of recovery of graph signals to solve the problem of background subtraction. The method is evaluated on the publicly available change detection, and scene background initialization databases. Experimental results show that GraphBGS outperforms unsupervised background subtraction algorithms in some challenges of the change detection dataset. And most significantly, this method outperforms generative adversarial networks in unseen videos in some sequences of the scene background initialization database.
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