Multi-object Foreground Extraction in Streaming Video using Low Rank Sparse Decomposition

Yogesh Sanku, Soumyo Bhattacharjee, Saumik Bhattacharya
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引用次数: 1

Abstract

Low rank sparse decomposition (LRSD) algorithm is a popular technique to split an input video in to a low rank form and a complementary sparse form. The decomposed low rank matrix signifies the background information while the sparse matrix captures the foreground information. The real power of the algorithm proposed is in the use of stationary camera systems, particularly in surveillance systems to extract moving objects efficiently for analyses. However, the existing LRSD algorithms are designed such that it can only work on the entire video cube, but not on streaming videos. This severely affects the usability of LRSD-based algorithms in real-world surveillance tasks. In this paper, we propose a novel LRSD decomposition algorithm that can deal with streaming video data. To the best of our knowledge, this is the first attempt to design an LRSD-based system to work on streaming videos with varying background conditions. Exhaustive experimental analyses have shown that the proposed framework can process the videos almost in real-time.
基于低秩稀疏分解的流视频多目标前景提取
低秩稀疏分解(LRSD)算法是一种将输入视频分割成低秩形式和互补稀疏形式的流行技术。分解后的低秩矩阵表示背景信息,稀疏矩阵表示前景信息。该算法的真正强大之处在于使用固定摄像机系统,特别是在监控系统中有效地提取运动物体进行分析。然而,现有的LRSD算法被设计成只能在整个视频立方体上工作,而不能在流媒体视频上工作。这严重影响了基于lrsd的算法在现实世界监控任务中的可用性。在本文中,我们提出了一种新的LRSD分解算法,可以处理流视频数据。据我们所知,这是第一次尝试设计一个基于lrsd的系统来处理不同背景条件下的流媒体视频。详尽的实验分析表明,所提出的框架几乎可以实时地处理视频。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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