Hang Li
ABSTRACT
To address the issue of background subtraction include shadow challenge, an online robust principal component analysis (RPCA) method with box constraint (BC-RPCA) has been proposed to detect moving object and accelerate the RPCA like method. First of all, the BC-RPCA method considers the input image sequences as low rank background, sparse foreground and moving shadow. Then the Augmented Lagrangian method is used to convert the box constraint into the objective function and rank-1 modification for thin SVD is also employed to accelerate the solver via alternating direction method of multipliers (ADMM). Finally, the experiments demonstrated the proposed method works effectively and has low computational complexity during real-time application.
- Zivkovic, Z. and Heijden, F.V.D. 2004. Recursive Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis & Machine Intelligence, 26, 5, (2004). 651--656. Google Scholar
Digital Library
- Bouwmans, T., Sobral, A., Javed, S., Jung, S.K. and Zahzah, E.H. 2016. Decomposition into low-rank plus additive matrices for background/foreground separation. Computer Science Review, 23 (November 2016). 1--71. Google ScholarDigital Library
- Guyon, C., Bouwmans, T. and Zahzah, E.H. 2012. Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis. InTech, (2012).Google Scholar
- Candes, E.J., Li, X., Ma, Y. and Wright, J. 2009. Robust principal component analysis? J. ACM, 58, 3, (2009). 11. Google ScholarDigital Library
- Zhou, Z., Li, X., Wright, J., Candes, E. and Ma, Y. 2010. Stable Principal Component Pursuit.. IEEE International Symposium on Information Theory Proceedings, (2010), 1518--1522.Google Scholar
- Becker, S., Candes, E., and Grant, M. 2011. TFOCS: flexible first-order methods for rank minimization. Low-rank Matrix Optimization Symposium, SIAM Conference on Optimization, (2011)Google Scholar
- Li, H., Zhang, Y., Wang, J., Xu, Y., Li, Y. and Pan, Z. 2015. Inequality-Constrained RPCA for Shadow Removal and Foreground Detection. IEICE Trans.inf. & Syst, 98, 6, (2015). 1256--1259.Google Scholar
- Rodriguez, P. and Wohlberg, B. 2014. Fast principal component pursuit via alternating minimization. in IEEE International Conference on Image Processing, (2014), 69--73.Google Scholar
- Rodriguez, P. and Wohlberg, B. 2016. Incremental Principal Component Pursuit for Video Background Modeling. Journal of Mathematical Imaging & Vision, 55 (2016). 1--18. Google ScholarDigital Library
- Javed, S., Bouwmans, T. and Jung, S.K. 2017. Improving OR-PCA via smoothed spatially-consistent low-rank modeling for background subtraction. in Symposium on Applied Computing, (2017), 89--94. Google ScholarDigital Library
- Javed, S., Mahmood, A., Bouwmans, T. and Jung, S.K. 2017. Superpixels-based Manifold Structured Sparse RPCA for Moving Object Detection. in British Machine Vision Conference, (2017).Google Scholar
- Brand, M. 2006. Fast low-rank modifications of the thin singular value decomposition. Linear Algebra & Its Applications, 415, 1, (2006) 20--30.Google ScholarCross Ref
- LIN, Z. C., LIU, R. S., SU Z. X. 2011. Linearized alternating direction method with adaptive penalty for low rank representation. Advances in 25th Neural Information Processing Systems, (Granada, Spain, New York, December, 2011). 612--620. Google ScholarDigital Library
- Sanin, A., Sanderson, C. and Lovell, B.C. 2012. Shadow detection: A survey and comparative evaluation of recent methods. Pattern Recognition, 45, 4, (2012). 1684--1695. Google ScholarDigital Library
- Sobral, A., Bouwmans, T. and Zahzah, E.H. 2016. LRSLibrary: Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos, Handbook on Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing. (2016).Google Scholar
Index Terms
- Background subtraction via online box constrained RPCA
Recommendations
Low-rank and sparse matrix decomposition via the truncated nuclear norm and a sparse regularizer
AbstractRecovering the low-rank and sparse components from a given matrix is a challenging problem that has many real applications. This paper proposes a novel algorithm to address this problem by introducing a sparse prior on the low-rank component. ...
Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements
Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing, and 3D modeling. Recently, motivated by compressive imaging, background subtraction from ...
Background subtraction via coherent trajectory decomposition
MM '13: Proceedings of the 21st ACM international conference on MultimediaBackground subtraction, the task to detect moving objects in a scene, is an important step in video analysis. In this paper, we propose an efficient background subtraction method based on coherent trajectory decomposition. We assume that the ...
Comments