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Title:Online robust principal component analysis for background subtraction: a system evaluation on Toyota car data
Author(s):Xu, Xingqian
Advisor(s):Huang, Thomas S.
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Online RPCA
Robust Principal Component Analysis (RPCA)
Abstract:Robust Principal Component Analysis (RPCA) methods have become very popular in the past ten years. Many publications show that RPCA provides good results for background subtraction problems. In this thesis, we further the exploration to online versions of RPCA algorithms. The proposed Online Robust Principal Component Analysis (ORPCA) is used to process big data in a more efficient way. We also test the algorithm performances on the Toyota car data set provided by the Toyota Motor Corporation. Meanwhile, a comprehensive comparison of the algorithm performance is also shown based on testing results and running efficiency.
Issue Date:2014-05-30
Rights Information:Copyright 2014 Xingqian Xu
Date Available in IDEALS:2014-05-30
Date Deposited:2014-05

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