Robust Dual-Graph Regularized Moving Object Detection

Jing Qin, Ruilong Shen, Ruihan Zhu, Biyun Xie

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


Moving object detection and its associated background-foreground separation have been widely used in a lot of applications, including computer vision, transportation and surveillance. Due to the presence of the static background, a video can be naturally decomposed into a low-rank background and a sparse foreground. Many regularization techniques, such as matrix nuclear norm, can therefore be imposed on the background. In the meanwhile, sparsity or smoothness based regularizations, such as total variation and ell_{1}, can be imposed on the foreground. Moreover, graph Laplacians are further used to capture the complicated geometry of background images. Recently, weighted regularization techniques including the weighted nuclear norm regularization have been proposed in the image processing community to promote adaptive sparsity while achieving efficient performance. In this paper, we propose a robust dual-graph regularized moving object detection model based on a new weighted nuclear norm regularization and spatiotemporal graph Laplacians, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on realistic body movement data sets have demonstrated the effectiveness of this method in separating moving objects from background, and the great potential in robotic applications.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Mechatronics and Automation, ICMA 2022
Number of pages6
ISBN (Electronic)9781665408523
StatePublished - 2022
Event19th IEEE International Conference on Mechatronics and Automation, ICMA 2022 - Guilin, Guangxi, China
Duration: Aug 7 2022Aug 10 2022

Publication series

Name2022 IEEE International Conference on Mechatronics and Automation, ICMA 2022


Conference19th IEEE International Conference on Mechatronics and Automation, ICMA 2022
CityGuilin, Guangxi

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS The research of Qin is supported by the NSF grant DMS-1941197, and the research of Shen, Zhu and Xie is supported by the University of Kentucky College of Engineering Young Alumni Philanthropy Council Funding.

Publisher Copyright:
© 2022 IEEE.


  • Moving object detection
  • alternating direction method of multipliers
  • graph Laplacian
  • sparsity
  • weighted nuclear norm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization


Dive into the research topics of 'Robust Dual-Graph Regularized Moving Object Detection'. Together they form a unique fingerprint.

Cite this