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

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 background subtraction, 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. Index Terms—State estimation, compressive video, background subtraction, sparsity, ℓ1 minimization, motion estimation.


Published in:
IEEE Transactions on Signal Processing, 64, 14, 3651-3666
Year:
2016
Publisher:
Piscataway, Institute of Electrical and Electronics Engineers
ISSN:
1053-587X
Keywords:
Laboratories:




 Record created 2015-12-22, last modified 2018-03-17

n/a:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)