Mota, João F. C.Deligiannis, NikosSankaranarayanan, Aswin C.Cevher, VolkanRodrigues, Miguel R. D.2015-03-162015-03-162015-03-16201610.1109/TSP.2016.2544744https://infoscience.epfl.ch/handle/20.500.14299/112486We 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-thefly. 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. Index Terms—State estimation, compressive video, background subtraction, sparsity, ℓ1 minimization, motion estimation.text::journal::journal article::research article