International audienceAccurate and efficient foreground detection is an important task in video surveillance system. The task becomes more critical when the background scene shows more variations, such as water surface, waving trees, varying illumination conditions, etc. Recently, Robust Principal Components Analysis (RPCA) shows a very nice framework for moving object detection. The background sequence is modeled by a low-dimensional subspace called low-rank matrix and sparse error constitutes the foreground objects. But RPCA presents the limitations of computational complexity and memory storage due to batch optimization methods, as a result it is difficult to apply for real-time system. To handle these challenges, this paper presents a robust foreground detection algorithm via Online Robust PCA (OR-PCA) using image decomposition along with continuous constraint such as Markov Random Field (MRF). OR-PCA with good initialization scheme using image decomposition approach improves the accuracy of foreground detection and the computation time as well. Moreover, solving MRF with graph-cuts exploits structural information using spatial neighborhood system and similarities to further improve the foreground segmentation in highly dynamic backgrounds. Experimental results on challenging datasets such as Wallflower, I2R, BMC 2012 and Change Detection 2014 dataset demonstrate that our proposed scheme significantly outperforms the state of the art approaches and works effectively on a wide range of complex background scenes

International audienceBackground subtraction process plays a very essential role for various computer vision tasks. The process becomes more critical when the input scene contains variation of pixels such as swaying trees, rippling of water, illumination variations, etc. Recent methods of matrix decomposition into low-rank (e.g., corresponds to the background) and sparse (e.g., constitutes the moving objects) components such as Robust Principal Component Analysis (RPCA), have been shown to be very efficient framework for background subtraction. However, when the size of the input data grows and due to the lack of sparsity-constraints, these methods cannot cope with the real-time challenges and always show a weak performance due to the erroneous foreground regions. In order to address the above mentioned issues, this paper presents a superpixel-based matrix decomposition method together with maximum norm (max-norm) regularizations and structured spar-sity constraints. The low-rank component estimated from each homogeneous region is more perfect, reliable, and efficient , since each superpixel provides different characteristics with a reduced value of rank. Online max-norm based matrix decomposition is employed on each segmented su-perpixel to separate the low rank and initial outliers support. And then, the structured sparsity constraints such as the generalized fussed lasso (GFL) are adopted for exploiting structural information continuously as the foreground pixels are both spatially connected and sparse. We propose an online single unified optimization framework for detecting foreground and learning the background model simultaneously. Rigorous experimental evaluations on challenging datasets demonstrate the superior performance of the proposed scheme in terms of both accuracy and computational time

The back-propagation(BP) dgorithm is widely used for finding optimum weights of multi-layer neural networks in many pattern recognition applications. However, the critical drawback of the BP algorithm is its slow convergence of error. The major reason for this slow convergence is the "premature saturation" which is a phenomenon that the error of a neural network stays almost constant for some period of time during learning. It is known to be caused by an inappropriate set of initial weights. In this paper, the probability of premature saturation in BP algorithm has been derived in terms of the maximum value of initial weights, the number of nodes in each layer. and the maximum slope of sigmoidal activation function; it has been verified by Monte Carlo simulation. Using this result, the premature saturation can be avoided with proper initial weight settings.

Accurate and efficient background subtraction is an important task in video surveillance system. The task becomes more critical when the background scene shows more variations, such as water surface, waving trees and lighting conditions, etc. Recently, Robust Principal Components Analysis (RPCA) shows a nice framework for moving object detection. The background sequence is modeled by a lowdimensional subspace called low-rank matrix and sparse error constitutes the foreground objects. But RPCA presents the limitations of computational complexity and memory storage due to batch optimization methods, as a result it is hard to apply for real-time system. To handle these challenges, this paper presents a robust background subtraction algorithm via Online Robust PCA (OR-PCA) using image decomposition. OR-PCA with image decomposition approach improves the accuracy of foreground detection and the computation time as well. Comprehensive simulations on challenging datasets such as Wallflower, I2R and Change Detection 2014 demonstrate that our proposed scheme significantly outperforms the state-of-the-art approaches and works effectively on a wide range of complex background scenes.

Background subtraction is an important task for various computer vision applications. The task becomes more critical when the background scene contains more variations, such as swaying trees and abruptly changing lighting conditions. Recently, robust principal component analysis (RPCA) has been shown to be a very efficient framework for moving-object detection. However, due to its batch optimization process, highdimensional data need to be processed. As a result, computational complexity, lack of features, weak performance, real-time processing, and memory issues arise in traditional RPCA-based approaches. To handle these, a background subtraction algorithm robust against global illumination changes via online robust PCA (OR-PCA) using multiple features together with continuous constraints, such as Markov random field (MRF), is presented. OR-PCA with automatic parameter estimation using multiple features improves the background subtraction accuracy and computation time, making it attractive for real-time systems. Moreover, the application of MRF to the foreground mask exploits structural information to improve the segmentation results. In addition, global illumination changes in scenes are tackled by using sum of the difference of similarity measure among features, followed by a parameter update process using a low-rank, multiple features model. Evaluation using challenging datasets demonstrated that the proposed scheme is a top performer for a wide range of complex background scenes. Javed et al.: Robust background subtraction to global illumination changes via multiple features-based. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/18/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx