Incremental Principal Component Pursuit for Video Background Modeling

While Principal Component Pursuit (PCP) is currently considered to be the state of the art method for video background modeling, it suffers from a number of limitations, including a high computational cost, a batch operating mode, and sensitivity to camera jitter. In this paper we propose a novel fully incremental PCP algorithm for video background modeling that is robust to translational and rotational jitter. It processes one frame at a time, obtaining similar results to standard batch PCP algorithms, while being able to deal with translational and rotational jitter. It also has extremely low memory footprint, and a computational complexity that allows almost real-time processing.

[1]  Graeme P. Penney,et al.  Retrospective Rigid Motion Correction in k-Space for Segmented Radial MRI , 2014, IEEE Transactions on Medical Imaging.

[2]  Amir Averbuch,et al.  Pseudopolar-based estimation of large translations, rotations, and scalings in images , 2005, IEEE Transactions on Image Processing.

[3]  G. Sapiro,et al.  A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography. , 2013, Journal of structural biology.

[4]  Brendt Wohlberg,et al.  Fast principal component pursuit via alternating minimization , 2013, 2013 IEEE International Conference on Image Processing.

[5]  Harold P. Benson,et al.  An Outer Approximation Algorithm for Generating All Efficient Extreme Points in the Outcome Set of a Multiple Objective Linear Programming Problem , 1998, J. Glob. Optim..

[6]  Thierry Bouwmans,et al.  Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance , 2014, Comput. Vis. Image Underst..

[7]  Atsushi Shimada,et al.  Spatio-temporal background models for object detection , 2014 .

[8]  Brendt Wohlberg,et al.  A Matlab implementation of a fast incremental principal component pursuit algorithm for Video Background Modeling , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[9]  Michael Unser,et al.  Convolution-based interpolation for fast, high-quality rotation of images , 1995, IEEE Trans. Image Process..

[10]  Brendt Wohlberg,et al.  Incremental Principal Component Pursuit for Video Background Modeling , 2015, Journal of Mathematical Imaging and Vision.

[11]  Laura Balzano,et al.  Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Thierry Bouwmans,et al.  Background Modeling and Foreground Detection for Video Surveillance , 2014 .

[13]  Tao Tao,et al.  Iterative Grassmannian optimization for robust image alignment , 2013, Image Vis. Comput..

[14]  Shiqian Ma,et al.  Convergence of Fixed-Point Continuation Algorithms for Matrix Rank Minimization , 2009, Found. Comput. Math..

[15]  Brendt Wohlberg,et al.  Endogenous convolutional sparse representations for translation invariant image subspace models , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[16]  John Wright,et al.  RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  M. Brand,et al.  Fast low-rank modifications of the thin singular value decomposition , 2006 .

[18]  Yi Ma,et al.  The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.

[19]  Brendt Wohlberg,et al.  Efficient convolutional sparse coding , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Katherine Mary Simonson,et al.  Robust Real-Time Change Detection in High Jitter , 2009 .