Abstract
We 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.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings |
Publisher | IEEE |
Pages | 3332-3336 |
Number of pages | 5 |
ISBN (Electronic) | 9781467369978 |
DOIs | |
Publication status | Published - Aug 2015 |
Event | 40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015 - Brisbane, Australia Duration: 19 Apr 2015 → 24 Apr 2015 |
Conference
Conference | 40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015 |
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Abbreviated title | ICASSP 2015 |
Country/Territory | Australia |
City | Brisbane |
Period | 19/04/15 → 24/04/15 |
Keywords
- background subtraction
- motion estimation
- online algorithms
- sparsity
- State estimation
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering