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
Link To Document