DMCA
Compressive Acquisition of Dynamic Scenes
Cached
Download Links
| Citations: | 37 - 10 self |
BibTeX
@MISC{Sankaranarayanan_compressiveacquisition,
author = {Aswin C. Sankaranarayanan and Pavan K. Turaga and Richard G. Baraniuk and et al.},
title = {Compressive Acquisition of Dynamic Scenes},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Nyquist rate. Despite significant progress in the theory and methods of CS, little headway has been made in compressive video acquisition and recovery. Video CS is complicated by the ephemeral nature of dynamic events, which makes direct extensions of standard CS imaging architectures and signal models infeasible. In this paper, we develop a new framework for video CS for dynamic textured scenes that models the evolution of the scene as a linear dynamical system (LDS). This reduces the video recovery problem to first estimating the model parameters of the LDS from compressive measurements, from which the image frames are then reconstructed. We exploit the low-dimensional dynamic parameters (the state sequence) and high-dimensional static parameters (the observation matrix) of the LDS to devise a novel compressive measurement strategy that measures only the dynamic part of the scene at each instant and accumulates measurements over time to estimate the static parameters. This enables us to considerably lower the compressive measurement rate considerably. We validate our approach with a range of experiments including classification experiments that highlight the effectiveness of the proposed approach.
Keyphrases
compressive acquisition dynamic scene video c compressive measurement rate direct extension new approach dynamic textured scene linear dynamical system new framework ourapproachwith arange ofexperimentsincludingclassification experiment state sequence signal model dynamic part novel compressive measurement strategy image frame model parameter video recovery problem dynamic event static parameter classical nyquist rate compressive measurement highdimensional static parameter standard c imaging architecture compressive sensing low-dimensional dynamic parameter observation matrix little headway compressive video acquisition significant progress sparse signal ephemeral nature
