Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy

M. Chen, X. Wei, Q. Yang, Qing Li, G. Wang, M.-H. Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

46 Citations (Scopus)

Abstract

© 1979-2012 IEEE. We propose a background subtraction algorithm using hierarchical superpixel segmentation, spanning trees and optical flow. First, we generate superpixel segmentation trees using a number of Gaussian Mixture Models (GMMs) by treating each GMM as one vertex to construct spanning trees. Next, we use the $M$-smoother to enhance the spatial consistency on the spanning trees and estimate optical flow to extend the $M$-smoother to the temporal domain. Experimental results on synthetic and real-world benchmark datasets show that the proposed algorithm performs favorably for background subtraction in videos against the state-of-the-art methods in spite of frequent and sudden changes of pixel values.
Original languageEnglish
Pages (from-to)1518-1525
Number of pages8
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume40
Issue number6
DOIs
Publication statusPublished - 1 Jun 2018
Externally publishedYes

Keywords

  • Background modeling
  • minimum spanning tree
  • optical flow
  • superpixel hierarchy
  • tracking

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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