Mota, João F. C.Deligiannis, NikosSankaranarayanan, Aswin C.Cevher, VolkanRodrigues, Miguel R. D.2015-12-222015-12-222015-12-22201610.1109/TSP.2016.2544744https://infoscience.epfl.ch/handle/20.500.14299/121878WOS:000377370500008We 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.Background subtractioncompressive videol(1) minimizationmotion estimationsparsitystate estimationtext::journal::journal article::research article