Online robust principal component analysis for background subtraction: a system evaluation on Toyota car data
Xu, Xingqian
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https://hdl.handle.net/2142/49503
Description
Title
Online robust principal component analysis for background subtraction: a system evaluation on Toyota car data
Author(s)
Xu, Xingqian
Issue Date
2014-05-30T16:47:30Z
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Thomas S.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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.
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