Thesis: Robust Low-rank and Sparse Decomposition for Moving Object Detection: From Matrices to Tensors by Andrews Cordolino Sobral

Here is what Andrews (whom we have followed for a while now) just sent me (Congratulations Dr. Sobral !)

Hi Igor,

First of all, I would like to congratulate you for your excellent blog.
I would like to share with you my thesis presentation about Robust Low-rank and Sparse Decomposition for Moving Object Detection: From Matrices to Tensors. I think this research work may be of interest to your blog. Please find below the slide presentation and the thesis manuscript:
Thesis presentation (SlideShare):
https://www.slideshare.net/andrewssobral/thesis-presentation-robust-lowrank-and-sparse-decomposition-for-moving-object-detection-from-matrices-to-tensors
Thesis manuscript (ResearchGate):
https://www.researchgate.net/publication/316967304_Robust_Low-rank_and_Sparse_Decomposition_for_Moving_Object_Detection_From_Matrices_to_Tensors
Many thanks,

Andrews Cordolino Sobral
Ph.D. on Computer Vision and Machine Learning
http://andrewssobral.wix.com/home

Here is the thesis abstract: Robust Low-rank and Sparse Decomposition for Moving Object Detection: From Matrices to Tensors

This thesis introduces the recent advances on decomposition into low-rank plus sparse matrices and tensors, as well as the main contributions to face the principal issues in moving object detection. First, we present an overview of the state-of-the-art methods for low-rank and sparse decomposition, as well as their application to background modeling and foreground segmentation tasks. Next, we address the problem of background model initialization as a reconstruction process from missing/corrupted data. A novel methodology is presented showing an attractive potential for background modeling initialization in video surveillance. Subsequently, we propose a double-constrained version of robust principal component analysis to improve the foreground detection in maritime environments for automated video-surveillance applications. The algorithm makes use of double constraints extracted from spatial saliency maps to enhance object foreground detection in dynamic scenes. We also developed two incremental tensor-based algorithms in order to perform background/foreground separation from multidimensional streaming data. These works address the problem of low-rank and sparse decomposition on tensors. Finally, we present a particular work realized in conjunction with the Computer Vision Center (CVC) at Autonomous University of Barcelona (UAB).

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