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Applications of Compressive Sensing to Surveillance Problems

  • Christopher Huff
  • Ram N. Mohapatra
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 91)

Abstract

In many surveillance scenarios, one concern that arises is how to construct an imager that is capable of capturing the scene with high fidelity. This could be problematic for two reasons: First, the optics and electronics in the camera may have difficulty in dealing with so much information; second, bandwidth constraints may pose difficulty in transmitting information from the imager to the user efficiently for reconstruction or realization. This paper is a study of the application of various compressive sensing methods to surveillance problems. It is based largely on the work of [7], with theory and algorithms presented in the same manner. We explore two of the seminal works in compressive sensing and present the key theorems and definitions from these two papers. We then survey three different surveillance scenarios and their respective compressive sensing solutions. The original contribution of this paper is the development of a distributed compressive sensing model.

Keywords

Compressive Sensing Difference Image Random Projection Multiple Camera Restricted Isometry Property 
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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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of Central FloridaOrlandoUSA

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