Minimax Based Regulation of Change Detection Threshold in Video-Surveillance Systems

  • Franco Oberti
  • Fabrizio Granelli
  • Carlo S. Regazzoni
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 573)

Abstract

Thanks to the increasing development of complex vision systems, it becomes strictly necessary to introduce quantitative performance evaluation methods. Such methods should make it possible both comparing results provided by different surveillance systems and selecting optimal parameters for each one, depending on the specific functionality of a system and on the particular characteristics of the monitored environment. In this contribution, it is shown that Receiver Operating Characteristics [1] (ROC) curves provide a well assessed tool that can be used for the above purpose. In literature ROC curves have been used for performance evaluation of image processing algorithms: in [2] for evaluation of edge detection algorithms and in [3] for evaluation of artificial neural networks for medical imaging.

Keywords

Coherence Assure 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    H. Van Trees, “Classical Detection and Estimation Theory — Detection, Estimation, and Modulation Theory”, John Wiley & Sons, Inc, 1968, pp. 19–46.Google Scholar
  2. [2]
    R.M. Haralick and J.S.J. Lee, “Contex Dependent Edge Detection and Evaluation”, Pattern Recognition, Vol.23, No.1, pp.1–19, 1990.CrossRefGoogle Scholar
  3. [3]
    K. Woods, and K.W. Bowyer, “Generating ROC Curves for Artificial Neural Networks”, IEEE Trans.on Medical Imaging, Vol. 16, No.3, June 1997, pp. 329–337.CrossRefGoogle Scholar
  4. [4]
    A. Tesei, A. Teschioni, C.S. Regazzoni, and G. Vernazza, “Long Memory Matching of Interacting Complex Objects from Real Image Sequences”, Proc. Conf. on Time Varying Image Processing and Moving Object Recognition, Florence (Italy), September 1996, pp. 283–286.Google Scholar
  5. [5]
    M. Bogaert, N. Chleq, P. Cornez, C.S. Regazzoni, A. Teschioni, M. Thonnat, “The PASSWORDS Project”, IEEE International Conference on Image Processing, Lausanne, September 1996, Vol.III, pp. 675–678.Google Scholar
  6. [6]
    “Advanced Video-based Surveillance Systems”, C.S Regazzoni, G. Vernazza and G. Fabri (Eds) — Kluwer Academic Publishers, 1998Google Scholar
  7. [7]
    T. Kanungo, M.Y. Jaisimha, J. Palmer, and R.M. Haralick, “A Methodology for Quantitative Performance Evaluation of Detection Algorithms”, IEEE Trans. On Image Processing, Vol.4, No.12, Dec.1995, pp.1667–1673.CrossRefGoogle Scholar
  8. [8]
    A. Teschioni and C. Regazzoni, “Performances Evaluation Strategies of an Image Processing System for Surveillance Applications”, in Advanced Video-based Surveillance Systems, Kluwer Academic Publishers, 1998, pp. 76–90.Google Scholar
  9. [9]
    F. Oberti, E. Siringa, “Performance Evaluation Criterion for Characterizing Video Surveillance Systems”, accepted for publication in Real-Time Imaging.Google Scholar

Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Franco Oberti
    • 1
  • Fabrizio Granelli
    • 1
  • Carlo S. Regazzoni
    • 1
  1. 1.Department of Biophysical and electronic EngineeringUniversity of GenoaGenovaItaly

Personalised recommendations