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.
This research was partially supported by the Office of Naval Research under the contracts N00014-09-1-1162 and N00014-07-1-0936, the U. S. Army Research Laboratory and the U. S. Army Research Office under grant number W911NF-09-1-0383, and the AFOSR under the contracts FA9550-09-1-0432 and FA9550-07-1-0301. The authors also thanks Prof. Mike Wakin for valuable discussions and Dr. Ashok Veeraraghavan for providing high speed video data.
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Sankaranarayanan, A.C., Turaga, P.K., Baraniuk, R.G., Chellappa, R. (2010). Compressive Acquisition of Dynamic Scenes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15549-9_10
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