Name
Title | ||
|---|---|---|
Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction. |
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 $\ell_1$-$\ell_1$ 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. |
Year | Venue | Field |
|---|---|---|
2015 | IEEE Transactions on Signal Processing | Iterative reconstruction,Background subtraction,Online algorithm,Pattern recognition,Matrix decomposition,Basis pursuit,Kalman filter,Artificial intelligence,Real image,Motion estimation,Mathematics |
DocType | Volume | Citations |
Journal | abs/1503.03231 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João F. C. Mota | 1 | 6 | 1.13 |
| Nikos Deligiannis | 2 | 37 | 5.20 |
| Aswin C. Sankaranarayanan | 3 | 770 | 51.51 |
| Volkan Cevher | 4 | 1 | 1.71 |
| Miguel R. D. Rodrigues | 5 | 1500 | 111.23 |