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Moving object detection by low rank approximation and l_1-TV regularization on RPCA framework

机译：在RPCA框架上通过低秩逼近和l_1-TV正则化检测运动对象

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摘要

The detection of moving objects and the subtraction of the scene background are significant tasks for intelligent video surveillance systems as it is one among the fundamental steps. Inspired by the challenging cases yet to be resolved in Moving Object Detection (MOD), a new formulation is done to detect moving objects from video sequences based on Robust Principal Component Analysis (RPCA) principle by adopting the regularization of Total Variation (TV) norm using a convergent convex optimization algorithm. While the nuclear norm exploits the low-rank property of background, the sparsity is enhanced by the l(1)-norm and the foreground spatial smoothness is explored by TV regularization. The goodness of this method lies in the reduced computational complexity, quickness and on the superiority acquired in quantitative evaluation based on F-measure, Recall and Precision with respect to the state of the art methods. (C) 2018 Elsevier Inc. All rights reserved.
机译：运动对象的检测和场景背景的减去对于智能视频监控系统是重要的任务，因为它是基本步骤之一。受运动对象检测（MOD）中尚待解决的具有挑战性的案例的启发，采用了稳健的主成分分析（RPCA）原理，通过采用总变化（TV）范数的正则化，采用一种新的公式从视频序列中检测运动对象使用收敛凸优化算法。核规范利用背景的低阶性质时，稀疏性通过l（1）规范增强，而前景空间平滑性通过TV正则化进行探索。该方法的优点在于，降低了计算复杂性，速度，并且相对于现有方法，还基于F-measure，Recall和Precision在定量评估中获得了优越性。 （C）2018 Elsevier Inc.保留所有权利。

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