Robust background subtraction via online robust PCA using image decomposition

Sajid Javed, Seon Ho Oh, Jun Hyeok Heo, Soon Ki Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Accurate and efficient background subtraction is an important task in video surveillance system. The task becomes more critical when the background scene shows more variations, such as water surface, waving trees and lighting conditions, etc. Recently, Robust Principal Components Analysis (RPCA) shows a nice framework for moving object detection. The background sequence is modeled by a low-dimensional subspace called low-rank matrix and sparse error constitutes the foreground objects. But RPCA presents the limitations of computational complexity and memory storage due to batch optimization methods, as a result it is hard to apply for real-time system. To handle these challenges, this paper presents a robust background subtraction algorithm via Online Robust PCA (OR-PCA) using image decomposition. OR-PCA with image decomposition approach improves the accuracy of foreground detection and the computation time as well. Comprehensive simulations on challenging datasets such as Wallflower, I2R and Change Detection 2014 demonstrate that our proposed scheme significantly outperforms the state-of-the-art approaches and works effectively on a wide range of complex background scenes.

Original languageBritish English
Title of host publicationProceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014
Pages105-110
Number of pages6
ISBN (Electronic)9781450330602
DOIs
StatePublished - 5 Oct 2014
Event2014 Conference on Research in Adaptive and Convergent Systems, RACS 2014 - Towson, United States
Duration: 5 Oct 20148 Oct 2014

Publication series

NameProceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014

Conference

Conference2014 Conference on Research in Adaptive and Convergent Systems, RACS 2014
Country/TerritoryUnited States
CityTowson
Period5/10/148/10/14

Keywords

  • Foreground detection
  • Image decomposition
  • Low-rank matrix
  • Online robust PCA

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