Applications of Compressive Sensing to Surveillance Problems

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


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.


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.


  1. 1.
    Baraniuk, R., Sankaranarayanan, A., Turaga, P., Chellappa, R.: Compressive acquisition of dynamic scenes. In: Proceedings of the 11th European Conference on Computer Vision: Part I, ECCV’10, pp. 129–142. Springer, Berlin (2010)Google Scholar
  2. 2.
    Bhagavatula, V.K., Haberfelde, T., Mahalanobis, A., Neifeld, M., Brady, D.: Off-axis sparse aperture imaging using phase optimization techniques for application in wide-area imaging systems. Appl. Opt. 48(28), 5212–5224 (2009)Google Scholar
  3. 3.
    Candes, E., Tao, T.: Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theor. 54(12), 5406–5425 (2006)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Drori, I., Donoho, D., Tsaig, Y., Starck, J.: Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit. Technical report (2006)Google Scholar
  5. 5.
    Duarte, M., Reddy, D., Baraniuk, R., Cevher, V., Sankaranarayanan, A., Chellappa, R.: Compressive sensing for background subtraction. In: Proceedings of the European Conference on Computer Vision (ECCV) (2008)Google Scholar
  6. 6.
    Huff, C., Muise, R.: Wide-area surveillance with multiple cameras using distributed compressive imaging. In: Proceedings of the SPIE (2011)Google Scholar
  7. 7.
    Huff, C.: Applications of compressive sensing to surveillance problems. Master’s thesis, University of Central Florida, Orlando (2012)Google Scholar
  8. 8.
    Muise, R.: Compressive imaging: an application. SIAM J. Imaging Sci. 2(4), 1255–1276 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Romberg, J., Candes, E., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207–1223 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Van Trees, H.: Detection, Estimation, and Modulation Theory. Part I. Wiley, New York (1968)zbMATHGoogle Scholar
  11. 11.
    Wakin, M.: A manifold lifting algorithm for multi-view compressive imaging. In: Proceedings of the Picture Coding Symposium (2009)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of Central FloridaOrlandoUSA

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