A basic algorithmic task in automated video surveillance is to separate background and foreground objects. Camera tampering, noisy videos, low frame rate, etc., pose difficulties in solving the problem. A general approach that classifies the tampered frames, and performs subsequent analysis on the remaining frames after discarding the tampered ones, results in loss of information. Several robust methods based on robust principal component analysis (PCA) have been introduced to solve this problem. To date, considerable effort has been expended to develop robust PCA via Principal Component Pursuit (PCP) methods with reduced computational cost and visually appealing foreground detection. However, the convex optimizations used in these algorithms do not scale well to real-world large datasets due to large matrix inversion steps. Also, an integral component of these foreground detection algorithms is singular value decomposition which is nonrobust. In this paper, we present a new video surveillance background modelling algorithm based on a new robust singular value decomposition technique rSVDdpd which takes care of both these issues. We also demonstrate the superiority of our proposed algorithm on a benchmark dataset and a new real-life video surveillance dataset in the presence of camera tampering. Software codes and additional illustrations are made available at the accompanying website rSVDdpd Homepage (https://subroy13.github.io/rsvddpd-home/)

翻译：自动视频监控中的一项基本算法任务是将背景与前景目标分离。摄像机篡改、噪声视频、低帧率等因素给解决该问题带来了困难。一种通用方法是对被篡改帧进行分类，并在丢弃被篡改帧后对剩余帧进行后续分析，但这会导致信息丢失。为解决此问题，已引入多种基于鲁棒主成分分析（PCA）的鲁棒方法。迄今为止，大量工作致力于通过主成分追踪（PCP）方法开发鲁棒PCA，以降低计算成本并实现视觉上吸引人的前景检测。然而，这些算法中使用的凸优化由于需要大规模矩阵求逆步骤，难以扩展到实际的大型数据集。此外，这些前景检测算法的一个核心组成部分是奇异值分解，这本身是不鲁棒的。在本文中，我们提出了一种基于新型鲁棒奇异值分解技术rSVDdpd的视频监控背景建模算法，该技术同时解决了这两个问题。我们还在基准数据集和存在摄像机篡改的新实际视频监控数据集上证明了所提算法的优越性。软件代码和额外示例可在配套网站rSVDdpd主页（https://subroy13.github.io/rsvddpd-home/）获取。