论文标题
GraphBGS：通过恢复图信号的背景减法
GraphBGS: Background Subtraction via Recovery of Graph Signals
论文作者
论文摘要
背景减法是计算机视觉中的基本预处理任务。由于静态和移动相机序列的背景变化，因此在实际场景中，此任务变得具有挑战性。文献中已经提出了几种用于背景减法的深度学习方法，并具有竞争性表演。但是，在看不见的视频测试时，这些模型显示出性能退化。他们需要大量数据以避免过度拟合。最近，基于图的算法已经成功地接近了无监督和半监督的学习问题。此外，将图形信号处理和半监督学习的理论结合在一起，从而导致机器学习领域的新见解。在本文中，在背景减法问题中介绍了图形信号的恢复概念。我们提出了一种称为Graph背景减法（GraphBGS）的新算法，该算法由：实例分割，背景初始化，图形构造，图形采样和一种从图形信号恢复理论中启发的半监督算法。我们的算法具有比深度学习方法所需的标签数据较少的优势，同时在同时具有竞争性结果：静态和移动的相机视频。在公开可用的变更检测（CDNET2014）和UCSD背景减法数据库中，GraphBG在几个具有挑战性的条件下，在几种具有挑战性的条件下，均优于无监督和监督方法。
Background subtraction is a fundamental pre-processing task in computer vision. This task becomes challenging in real scenarios due to variations in the background for both static and moving camera sequences. Several deep learning methods for background subtraction have been proposed in the literature with competitive performances. However, these models show performance degradation when tested on unseen videos; and they require huge amount of data to avoid overfitting. Recently, graph-based algorithms have been successful approaching unsupervised and semi-supervised learning problems. Furthermore, the theory of graph signal processing and semi-supervised learning have been combined leading to new insights in the field of machine learning. In this paper, concepts of recovery of graph signals are introduced in the problem of background subtraction. We propose a new algorithm called Graph BackGround Subtraction (GraphBGS), which is composed of: instance segmentation, background initialization, graph construction, graph sampling, and a semi-supervised algorithm inspired from the theory of recovery of graph signals. Our algorithm has the advantage of requiring less labeled data than deep learning methods while having competitive results on both: static and moving camera videos. GraphBGS outperforms unsupervised and supervised methods in several challenging conditions on the publicly available Change Detection (CDNet2014), and UCSD background subtraction databases.
