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Adaptive-rate reconstruction of time-varying signals with application in compressive foreground extraction This publication appears in: IEEE Transactions on Signal Processing Authors: J. Mota, N. Deligiannis, A. Sankaranarayanan, V. Cevher and M. Rodrigues Volume: 64 Issue: 14 Pages: 3651-3666 Publication Date: Jul. 2016
Abstract: 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 compressive video background subtraction, a problem that can be 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 with respect to state-of-the-art compressive background subtraction schemes.
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