Fast Dual-Graph Regularized Background Foreground Separation

Longxiu Huang, Jing Qin

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

Abstract

Foreground-background separation is a crucial task in various applications such as computer vision, robotics, and surveillance. Robust Principal Component Analysis (RPCA) is a popular method for this task, which considers the static background as the low-rank component and the moving objects in the foreground as the sparse component. To enhance the performance of RPCA, graph regularization is typically used to incorporate the sophisticated geometry of the background and temporal correlation. However, handling the graph Laplacians can be challenging due to the substantial number of data points. In this study, we propose a novel dual-graph regularized foreground-background separation model based on Sobolev smoothness. Our model is solved using a fast numerical algorithm based on the matrix CUR decomposition. Experimental results on real datasets demonstrate that our proposed algorithm achieves state-of-the-art computational efficiency.

Original languageEnglish
Title of host publication2023 International Conference on Sampling Theory and Applications, SampTA 2023
ISBN (Electronic)9798350328851
DOIs
StatePublished - 2023
Event2023 International Conference on Sampling Theory and Applications, SampTA 2023 - New Haven, United States
Duration: Jul 10 2023Jul 14 2023

Publication series

Name2023 International Conference on Sampling Theory and Applications, SampTA 2023

Conference

Conference2023 International Conference on Sampling Theory and Applications, SampTA 2023
Country/TerritoryUnited States
CityNew Haven
Period7/10/237/14/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • background foreground separation
  • CUR decomposition
  • graph regularization
  • motion detection
  • Robust principal component analysis

ASJC Scopus subject areas

  • Discrete Mathematics and Combinatorics
  • Statistics and Probability
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Signal Processing

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