Adaptive-Rate Reconstruction of Time-Varying Signals with Application in Compressive Foreground ExtractionReport as inadecuate




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Published in: IEEE Transactions on Signal Processing (ISSN: 1053-587X), vol. 64, num. 14, p. 3651-3666 Piscataway: Institute of Electrical and Electronics Engineers, 2016

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.

Keywords: Background subtraction ; compressive video ; l(1) minimization ; motion estimation ; sparsity ; state estimation Reference EPFL-ARTICLE-214741doi:10.1109/TSP.2016.2544744View record in Web of Science





Author: Mota, João F. C.; Deligiannis, Nikos; Sankaranarayanan, Aswin C.; Cevher, Volkan; Rodrigues, Miguel R. D.

Source: https://infoscience.epfl.ch/record/214741?ln=en







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