A Simple Method for Eccentric Event Espial Using Mahalanobis Metric

  • Md. Haidar Sharif
  • Chabane Djeraba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


The paper presents an approach, which detects eccentric events in real time surveillance video systems (e.g., escalators), based on optical flow analysis of multitude behavour followed by Mahalanobis and χ 2 metrics. The video frames are flagged as normal or eccentric established on the statistical classification of the distribution of Mahalanobis distances of the normalized spatiotemporal information of optical flow vectors. Those optical flow vectors are computed from the small blocks of the explicit region of successive frames namely Region of Interest Image (RII), which is discovered by RII Map (RIIM). The RIIM is obtained from specific treatment of foreground segmentation of moving subjects. The method essentially has been tested against a single camera data-set.


Video Stream Mahalanobis Distance Abnormal Event Spatiotemporal Information Reduce Processing Time 
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.
    Commission, U.C.P.S.: Cpsc document #5111: Escalator safety. The United States Consumer Product Safety Commission (USCPSC), Washington, DC (2003)Google Scholar
  2. 2.
    Kawai, Y., Takahashi, M., Sano, M., Fujii, M.: High-level feature extraction and surveillance event detection. In: NHK STRL at TRECVID (2008)Google Scholar
  3. 3.
    Hao, S., Yoshizawa, Y., Yamasaki, K., Shinoda, K., Furui, S.: Tokyo Tech at TRECVID (2008)Google Scholar
  4. 4.
    Orhan, O.B., Hochreiter, J., Poock, J., Chen, Q., Chabra, A., Shah, M.: Content based copy detection and surveillance event detection. In: UCF at TRECVID (2008)Google Scholar
  5. 5.
    Andrade, E.L., Blunsden, S., Fisher, R.B.: Hidden markov models for optical flow analysis in crowds. In: ICPR 2006, pp. 460–463 (2006)Google Scholar
  6. 6.
    Andrade, E.L., Blunsden, S., Fisher, R.B.: Modelling crowd scenes for event detection. In: ICPR 2006, pp. 175–178 (2006)Google Scholar
  7. 7.
    Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11, 172–185 (2005)CrossRefGoogle Scholar
  8. 8.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI 1981, pp. 674–679 (1981)Google Scholar
  9. 9.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR 1994, pp. 593–600 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Md. Haidar Sharif
    • 1
  • Chabane Djeraba
    • 1
  1. 1.University of Sciences and Technologies of Lille (USTL)France

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