Work

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

Year: 2014

Type: article

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.

Source: Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)

Author: Xingqian Xu

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2567875966

FWCI: 0.771

Cites: 12

Cited by: 5

Related to: 10