CS-RPCA: Clustered Sparse RPCA for Moving Object Detection

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6 Scopus citations

Abstract

Moving object detection (MOD) is an important step for many computer vision applications. In the last decade, it is evident that RPCA has shown to be a potential solution for MOD and achieved a promising performance under various challenging background scenes. However, because of the lack of different types of features, RPCA still shows degraded performance in many complicated background scenes such as dynamic backgrounds, cluttered foreground objects, and camouflage. To address these problems, this paper presents a Clustered Sparse RPCA (CS-RPCA) for MOD under challenging environments. The proposed algorithm extracts multiple features from video sequences and then employs RPCA to get the low-rank and sparse component from each representation. The sparse subspaces are then emerged into a common sparse component using Grassmann manifold. We proposed a novel objective function which computes the composite sparse component from multiple representations and it is solved using non-negative matrix factorization method. The proposed algorithm is evaluated on two challenging datasets for MOD. Results demonstrate excellent performance of the proposed algorithm as compared to existing state-of-the-art methods.

Original languageBritish English
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages3209-3213
Number of pages5
ISBN (Electronic)9781728163956
DOIs
StatePublished - Oct 2020
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: 25 Sep 202028 Sep 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period25/09/2028/09/20

Keywords

  • Background Subtraction
  • Low-rank modeling
  • Robust PCA

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