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Compressive Acquisition of Dynamic Scenes

  • Aswin C. Sankaranarayanan
  • Pavan K. Turaga
  • Richard G. Baraniuk
  • Rama Chellappa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

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.

Keywords

Compressive Sense State Sequence Linear Dynamical System Video Model Dynamic Scene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Aswin C. Sankaranarayanan
    • 1
  • Pavan K. Turaga
    • 2
  • Richard G. Baraniuk
    • 1
  • Rama Chellappa
    • 2
  1. 1.Rice UniversityHoustonUSA
  2. 2.University of MarylandCollege ParkUSA

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