Abnormal Change Detection of Image Quality Metric Series Using Diffusion Process and Stopping Time Theory

  • Haoting Liu
  • Jian Cheng
  • Hanqing Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)


To evaluate and monitor the Image Quality (IQ) change of a surveillance sequence for video analysis, a diffusion process and stopping time theory based model is presented in this paper because they can describe the uncertainty of an actual stochastic series rationally. First, we calculate the IQ metric for each frame. Then we connect all these discrete data together to form an Image Quality Metric Series (IQMS). After that, a non-parametric estimation technique based diffusion process model is used to fit the fluctuation path of the IQMS. Finally, a stopping time based model is employed to detect the abnormal change. Different to the conventional diffusion process method, the function forms of our model are estimated online and affirmed by an evaluation result of the hypothesis test. Comparing with the traditional time series model, such as the ARMA model, extensive experiments have proved that this method is effective and efficient on detecting the abnormal change.


Video surveillance image quality abnormal change detection  diffusion process stopping time 


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  1. 1.
    Valera, M., Velastin, S.A.: Intelligent Distributed Surveillance Systems: a Review. IEE Proc. Vision, Image and Sig. Proc. 152, 192–204 (2005)CrossRefGoogle Scholar
  2. 2.
    Narasimhan, S.G., et al.: All the Images of an Outdoor Scene. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 148–162. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Ke, Y., et al.: The Design of High-level Features for Photo Quality Assessment. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 419–426. IEEE Press, New York (2006)Google Scholar
  5. 5.
    Wei, X.-H., et al.: An Image Quality Estimation Model Based on HVS. In: IEEE Region 10 Conference, pp. 1–4. IEEE Press, New York (2006)Google Scholar
  6. 6.
    Kirchgässner, G., Wolters, J.: Introduction to Model Time Series Analysis. Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Karlin, S., Taylor, H.M.: A Second Course in Stochastic Processes. Academic, New York (1981)Google Scholar
  8. 8.
    Jeantheau, T.: A Link between Complete Models with Stochastic Volatility and ARCH Models. Fin. and Stochastics 8, 111–131 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Pospisil, L., et al.: Formulas for Stopped Diffusion Processes with Stopping Times Based on Drawdowns and Drawups. Stochastic Proc. and their Application 199, 2563–2578 (2009)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Gutiérrez, R., et al.: A new Stochastic Gompertz Diffusion Process with Threshold Parameter: Computational Aspects and Applications. App. Mathematics and Computation 183, 738–747 (2006)zbMATHCrossRefGoogle Scholar
  11. 11.
    Gutiérrez, R., et al.: The Trend of the Total Stock of the Private Car-petrol in Spain: Stochastic Modeling Using a new Gamma Diffusion Process. App. Energy 86, 18–24 (2009)CrossRefGoogle Scholar
  12. 12.
    Jang, J.: Jump Diffusion Processes and their Application in Insurance and Finance. Ins. Mathematics & Econ. 41, 62–70 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Yacine, A.-S.: Testing Continuous Time Models of the Spot Interest Rate. Rev. Fin. Studies 9, 385–426 (1996)CrossRefGoogle Scholar
  14. 14.
    Hong, Y.-M., Li, H.-T.: Nonparametric Specification Testing for Continuous Time Models with Application to Spot Interest Rates. Rev. Fin. Studies 18, 37–84 (2005)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Bibby, B.M., et al.: Estimating Functions for Discretely Sampled Diffusion Type Models. Handbook of Financial Econometrics. Elsevier Science Ltd., Amsterdam (2005)Google Scholar
  16. 16.
    Jiang, G.J., Knight, J.L.: Parametric Versus Nonparametric Estimation of Diffusion Processes – a Monte Carlo Comparison. J. Computational Finance 2, 5–38 (1999)Google Scholar
  17. 17.
    Rao, P.: Statisticians Inference for Diffusion Type Processes. Wiley, Chichester (1999)Google Scholar
  18. 18.
    Xiao, H.: Similarity Search and Outlier Detection in Time Series. Ph. D dissertation, Department of Computer and Information Technique, FuDan University (2005)Google Scholar
  19. 19.
    Poor, H.V., Hadjiliadis, O.: Quickest Detection. Cambridge University, Cambridge (2009)zbMATHGoogle Scholar
  20. 20.
    Nikolopoulos, C.V., Yannacopoulos, A.N.: A Model for Optimal Stopping in Advertisement. Nonlinear Anal. Real World Applications (2009) (in press)Google Scholar
  21. 21.
    Xu, K.-L.: Empirical Likelihood-based Inference for Nonparametric Recurrent Diffusions. J. Econometrics 153, 65–82 (2009)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Lu, P.-F., et al.: A Predictive Coding Using Markov Chain. In: IEEE International Conference on Signal Processing, pp. 1151–1154. IEEE Press, New York (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Haoting Liu
    • 1
    • 2
  • Jian Cheng
    • 1
  • Hanqing Lu
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Department of Space ErgonomicsAstronaut Research & Training Center of ChinaBeijingChina

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