摘要翻译：
背景减除是大多数视频检测系统的首要任务。背景减除中最重要的部分是背景建模，这是不同算法中最常见的部分。在这方面，本文以一种高效的计算方法来解决背景建模问题，这对于当前爆发的来自高分辨率多通道视频的“大数据”处理具有重要意义。我们的模型是基于自然图像中背景位于一个低维子空间的假设。我们在一个低秩矩阵完备框架中建立并解决了这个问题。在对背景进行建模时，我们利用了面向扩展的Frank-Wolfe算法来求解一个定义的凸优化问题。我们在背景模型challenge(BMC)和Stuttgart人工背景减法(SABS)数据集上对我们的快速鲁棒矩阵完成(fRMC)方法进行了评估。并与用非精确增广拉格朗日乘子(IALM)求解的鲁棒主成分分析(RPCA)和低秩鲁棒矩阵完备(RMC)方法进行了比较。结果表明，在减去背景以检测场景中的运动物体时，计算速度比使用IALM求解器快至少两倍，同时在某些挑战中具有相当的精度，甚至更好。
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英文标题：
《Background Subtraction via Fast Robust Matrix Completion》
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作者：
Behnaz Rezaei and Sarah Ostadabbas
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最新提交年份：
2017
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Background subtraction is the primary task of the majority of video inspection systems. The most important part of the background subtraction which is common among different algorithms is background modeling. In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel videos. Our model is based on the assumption that background in natural images lies on a low-dimensional subspace. We formulated and solved this problem in a low-rank matrix completion framework. In modeling the background, we benefited from the in-face extended Frank-Wolfe algorithm for solving a defined convex optimization problem. We evaluated our fast robust matrix completion (fRMC) method on both background models challenge (BMC) and Stuttgart artificial background subtraction (SABS) datasets. The results were compared with the robust principle component analysis (RPCA) and low-rank robust matrix completion (RMC) methods, both solved by inexact augmented Lagrangian multiplier (IALM). The results showed faster computation, at least twice as when IALM solver is used, while having a comparable accuracy even better in some challenges, in subtracting the backgrounds in order to detect moving objects in the scene.
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PDF链接：
https://arxiv.org/pdf/1711.01218