Compressive sensing for background subtraction

Volkan Cevher, Aswin Sankaranarayanan, Marco F. Duarte, Dikpal Reddy, Richard G. Baraniuk, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

179 Scopus citations

Abstract

Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reconstructed from a small set of random projections, provided that the signal is sparse in some basis, e.g., wavelets. In this paper, we describe a method to directly recover background subtracted images using CS and discuss its applications in some communication constrained multi-camera computer vision problems. We show how to apply the CS theory to recover object silhouettes (binary background subtracted images) when the objects of interest occupy a small portion of the camera view, i.e., when they are sparse in the spatial domain. We cast the background subtraction as a sparse approximation problem and provide different solutions based on convex optimization and total variation. In our method, as opposed to learning the background, we learn and adapt a low dimensional compressed representation of it, which is sufficient to determine spatial innovations; object silhouettes are then estimated directly using the compressive samples without any auxiliary image reconstruction. We also discuss simultaneous appearance recovery of the objects using compressive measurements. In this case, we show that it may be necessary to reconstruct one auxiliary image. To demonstrate the performance of the proposed algorithm, we provide results on data captured using a compressive single-pixel camera. We also illustrate that our approach is suitable for image coding in communication constrained problems by using data captured by multiple conventional cameras to provide 2D tracking and 3D shape reconstruction results with compressive measurements.

Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
PublisherSpringer-Verlag
Pages155-168
Number of pages14
EditionPART 2
ISBN (Print)3540886850, 9783540886853
DOIs
StatePublished - 2008
Event10th European Conference on Computer Vision, ECCV 2008 - Marseille, France
Duration: Oct 12 2008Oct 18 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5303 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th European Conference on Computer Vision, ECCV 2008
CountryFrance
CityMarseille
Period10/12/0810/18/08

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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