Adaptive-Rate Reconstruction of Time-Varying Signals with Application in Compressive Foreground Extraction

João F. C. Mota, Nikos Deligiannis, Aswin C. Sankaranarayanan, Volkan Cevher, Miguel Raul Dias Rodrigues

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements. The signals are assumed sparse, with unknown support, and evolve over time according to a generic nonlinear dynamical model. Our algorithm, based on recent theoretical results for ℓ1-ℓ1 minimization, is recursive and computes the number of measurements to be taken at each time on-the-fly. As an example, we apply the algorithm to online compressive video foreground extraction, a problem stated as follows: given a set of measurements of a sequence of images with a static background, simultaneously reconstruct each image while separating its foreground from the background. The performance of our method is illustrated on sequences of real images. We observe that it allows a dramatic reduction in the number of measurements or reconstruction error with respect to state-of-the-art compressive background subtraction schemes.

Original languageEnglish
Article number7442140
Pages (from-to)3651-3666
Number of pages16
JournalIEEE Transactions on Signal Processing
Issue number14
Publication statusPublished - 15 Jul 2016


  • 1 minimization
  • background subtraction
  • compressive video
  • motion estimation
  • sparsity
  • State estimation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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