Compressive Sensing of Dynamical Scenes with demo code

Aswin Sankaranarayanan just let me know that the  Compressive Acquisition of Dynamical Scenes page is up with an attendant demo. Aswin  also mentions that:

The code is a bit specialized for normal purposes since it involves a special measurement process. The file demo.m is set to automatically run, simulate compressive measurements and then the video recovery process. The code is also a bit on the slower side --- as is almost the case when we deal with videos.

The attendant paper is: Compressive Acquisition of Dynamical Scenes by  Aswin C. Sankaranarayanan , Pavan Turaga, Rama Chellappa, Richard Baraniuk. The abstract reads:

Abstract. Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates signifi cantly below the classical Nyquist rate. Despite signifi cant 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 di cult. 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, and then reconstructing the image frames. 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 lower the compressive measurement rate considerably. We validate our approach with a range of experiments involving both video recovery, sensing hyper-spectral data, and classi cation of dynamic scenes from compressive data. Together, these applications demonstrate the eff ectiveness of the approach.

Thanks Aswin.

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