Moving object detection by low rank approximation and l(1)-TV regularization on RPCA framework

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.