Compressive Acquisition of Dynamic Scenes Aswin C. Sankaranarayanan 1 , Pavan K. Turaga 2 , Richard G. Baraniuk 1 , and Rama Chellappa 2 1 Rice University, Houston, TX 77005, USA 2 University of Maryland, College Park, MD 20740, USA Abstract. Compressive sensing (CS) is a new approach for the acqui- sition 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 exten- sions 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 dy- namical system (LDS). This reduces the video recovery problem to first estimating the model parameters of the LDS from compressive measure- ments, 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 measure- ments 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. 1 Introduction Recent advances in the field of compressive sensing (CS) [4] have led to the development of imaging devices that sense at measurement rates below than the Nyquist rate. Compressive sensing exploits the property that the sensed signal is often sparse in some transform basis in order to recover it from a small number of linear, random, multiplexed measurements. Robust signal recovery is possible from a number of measurements that is proportional to the sparsity level of the signal, as opposed to its ambient dimensionality. While there has 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 Labo- ratory 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 Veer- araghavan for providing high speed video data.