Fast Adaptive Robust Subspace Tracking for Online Background Subtraction

We propose a fast-adapted subspace tracking algorithm for background subtraction in video surveillance. While background scenes are modelled as a linear combination of basis images, foreground scenes are regarded as a sparse image. Every time a video frame streams in, two alternating procedures are repeatedly done: basis images are updated by a recursive least square algorithm and foreground images are extracted by solving the L1-minimization problem. In the aspect that this algorithm is basically an online algorithm fast-adapted to background change, which is very much required for real-time video surveillance, it is the most efficient among all the algorithms that are based on both low-rank condition (for background modelling) and sparsity condition (for foreground modelling).

[1]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[2]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[3]  Namrata Vaswani,et al.  Recursive sparse recovery in large but correlated noise , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[4]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[5]  Jingdong Wang,et al.  A Probabilistic Approach to Robust Matrix Factorization , 2012, ECCV.

[6]  Namrata Vaswani,et al.  Recursive robust PCA or recursive sparse recovery in large but structured noise , 2013, ICASSP.

[7]  Silvere Bonnabel,et al.  Linear Regression under Fixed-Rank Constraints: A Riemannian Approach , 2011, ICML.

[8]  Bertrand Vachon,et al.  Statistical Background Modeling for Foreground Detection: A Survey , 2010 .

[9]  Qi Tian,et al.  Foreground object detection from videos containing complex background , 2003, MULTIMEDIA '03.

[10]  Marc Van Droogenbroeck,et al.  ViBE: A powerful random technique to estimate the background in video sequences , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[12]  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.