Adaptive-Rate Compressive Sensing Using Side Information

Garrett Warnell, Sourabh Bhattacharya, Rama Chellappa, Tamer Basar

Research output: Contribution to journalArticlepeer-review

Abstract

We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information. The first method uses extra cross-validation measurements, and the second one exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients that comprises the images in the video sequence. Instead, we use the side information to predict the number of significant coefficients in the signal at the next time instant. We develop our techniques in the specific context of background subtraction using a spatially multiplexing CS camera such as the single-pixel camera. For each image in the video sequence, the proposed techniques specify a fixed number of CS measurements to acquire and adjust this quantity from image to image. We experimentally validate the proposed methods on real surveillance video sequences.

Original languageEnglish (US)
Article number7156150
Pages (from-to)3846-3857
Number of pages12
JournalIEEE Transactions on Image Processing
Volume24
Issue number11
DOIs
StatePublished - Nov 1 2015

Keywords

  • Compressive sensing
  • background subtraction
  • cross validation
  • opportunistic sensing

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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