Mota, JoãoDeligiannis, NikosSankaranarayanan, Aswin C.Cevher, VolkanRodrigues, Miguel2015-02-162015-02-162015-02-16201510.1109/ICASSP.2015.7178588https://infoscience.epfl.ch/handle/20.500.14299/110967We propose a recursive algorithm for estimating time-varying signals from a few linear measurements. The signals are assumed sparse, with unknown support, and are described by a dynamical model. In each iteration, the algorithm solves an ℓ1-ℓ1 minimization problem and estimates the number of measurements that it has to take at the next iteration. These estimates are computed based on recent theoretical results for ℓ1-ℓ1 minimization. We also provide sufficient conditions for perfect signal reconstruction at each time instant as a function of an algorithm parameter. The algorithm exhibits high performance in compressive tracking on a real video sequence, as shown in our experimental results. Index Terms— State estimation, sparsity, background subtraction, motion estimation, online algorithmstext::conference output::conference proceedings::conference paper