• DocumentCode
    3630123
  • Title

    Detecting Suspicious Behavior in Surveillance Images

  • Author

    Daniel Barbará;Carlotta Domeniconi;Zoran Duric;Maurizio Filippone;Richard Mansfield;Edgard Lawson

  • Author_Institution
    Comput. Sci. Dept., George Mason Univ., Fairfax, VA
  • fYear
    2008
  • Firstpage
    891
  • Lastpage
    900
  • Abstract
    We introduce a novel technique to detect anomalies in images. The notion of normalcy is given by a baseline of images, under the assumption that the majority of such images is normal. The key of our approach is a featureless probabilistic representation of images, based on the length of the codeword necessary to represent each image. Such codeword´s lengths are then used for anomaly detection based on statistical testing. Our techniques were tested on synthetic and real data sets. The results show that our approach can achieve high true positive and low false positive rates.
  • Keywords
    "Surveillance","Object detection","Computer science","Statistical analysis","Data mining","Conferences","Electronic mail","Testing","Information theory","Probability"
  • Publisher
    ieee
  • Conference_Titel
    Data Mining Workshops, 2008. ICDMW ´08. IEEE International Conference on
  • ISSN
    2375-9232
  • Electronic_ISBN
    2375-9259
  • Type

    conf

  • DOI
    10.1109/ICDMW.2008.36
  • Filename
    4734020