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Real Time Motion Changes for New Event Detection and Recognition

  • Konstantinos Avgerinakis
  • Alexia Briassouli
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

An original approach for real time detection of changes in motion is presented, for detecting and recognizing events. Current video change detection focuses on shot changes, based on appearance, not motion. Changes in motion are detected in pixels that are found to be active, and this motion is input to sequential change detection, which detects changes in real time. Statistical modeling of the motion data shows that the Laplace provides the most accurate fit. This leads to reliable detection of changes in motion for videos where shot change detection is shown to fail. Once a change is detected, the event is recognized based on motion statistics, size, density of active pixels. Experiments show that the proposed method finds meaningful changes, and reliable recognition.

Keywords

False Alarm Activity Area Change Detection Laplace Distribution Illumination Variation 
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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References

  1. 1.
    Chavez, G.C., Cord, M., Philip-Foliguet, S., Precioso, F., de Araujo, A.: Robust scene cut detection by supervised learning. In: EUPISCO (2006)Google Scholar
  2. 2.
    Ajay, D., Radhakrishan, R., Peker, K.: Video summarization using descriptors of motion activity: a motion activity based approach to key-frame extraction from video shots. J. Electronic Imaging 10, 909–916 (2001)CrossRefGoogle Scholar
  3. 3.
    Aach, T., Kaup, A., Mester, R.: Statistical model-based change detection in moving video. Signal Processing 31, 165–180 (1993)CrossRefzbMATHGoogle Scholar
  4. 4.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 780–785 (1997)CrossRefGoogle Scholar
  5. 5.
    Hassouni, M., Cherifi, H., Aboutajdine, D.: Hos-based image sequence noise removal. IEEE Transactions on Image Processing 15, 572–581 (2006)CrossRefGoogle Scholar
  6. 6.
    Giannakis, G., Tsatsanis, M.K.: Time-domain tests for Gaussianity and time-reversibility. IEEE Transactions on Signal Processing 42, 3460–3472 (1994)CrossRefGoogle Scholar
  7. 7.
    Briassouli, A., Kompatsiaris, I.: Robust temporal activity templates using higher order statistics. IEEE Transactions on Image Processing (to appear)Google Scholar
  8. 8.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1999 (1999)Google Scholar
  9. 9.
    Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27, 773–780 (2006)CrossRefGoogle Scholar
  10. 10.
    Dragalin, V.P.: Optimality of a generalized cusum procedure in quickest detection problem. In: Statistics and Control of Random Processes: Proceedings of the Steklov Institute of Mathematics, pp. 107–120. Providence, Rhode Island (1994)Google Scholar
  11. 11.
    Moustakides, G.V.: Optimal stopping times for detecting changes in distributions. Ann. Statist. 14, 1379–1387 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Page, E.S.: Continuous inspection scheme. Biometrika 41, 100–115 (1954)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Muthukrishnan, S., van den Berg, E., Wu, Y.: Sequential change detection on data streams. In: ICDM Workshop on Data Stream Mining and Management, Omaha NE (2007)Google Scholar
  14. 14.
    Lelescu, D., Schonfeld, D.: Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream. IEEE Transactions on Image Processing 5, 106–117 (2003)Google Scholar
  15. 15.
    Bansal, R.K., Papantoni-Kazakos, P.: An algorithm for detecting a change in a stochastic process. IEEE Transactions on Information Theory 32, 227–235 (1986)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Konstantinos Avgerinakis
    • 1
  • Alexia Briassouli
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Informatics and Telematics Institute, Centre for Research and TechnologyThessalonikiGreece

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