Online robust principal component analysis for background subtraction: a system evaluation on Toyota car data

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Xu, Xingqian

Contributors dc:contributor

• Huang, Thomas S.

• Online RPCA
• Robust Principal Component Analysis (RPCA)

Statement dc:rights

• Copyright 2014 Xingqian Xu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/49503

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/49503