GraphBGS：通过图形信号恢复的背景减除
GraphBGS: Background Subtraction via Recovery of Graph Signals
摘要：
背景减除是计算机视觉中一项基本的预处理任务。由于静态和移动相机序列的背景变化，该任务在真实场景中变得具有挑战性。文献中已经提出了几种用于背景减除的深度学习方法，它们具有竞争性的性能。然而，这些模型在对看不见的视频进行测试时表现出性能下降;它们需要大量的数据来避免过拟合。最近，基于图的算法已经成功地接近无监督和半监督学习问题。此外，图信号处理理论和半监督学习的结合，导致在机器学习领域的新见解。本文在背景减除问题中引入了图形信号恢复的概念。本文提出了一种新的图背景减法（GraphBGS）算法，该算法包括：实例分割、背景初始化、图构造、图采样和一种受图信号恢复理论启发的半监督算法。我们的算法具有比深度学习方法需要更少标记数据的优势，同时在静态和移动摄像机视频上都具有竞争力的结果。GraphBGS在公开的变化检测（CDNet 2014）和UCSD背景减除数据库上的几个具有挑战性的条件下优于无监督和监督方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
