Detecting suspicious behavior in surveillance images

Daniel Barbará*, Carlotta Domeniconi, Zoran Duriâ, Maurizio Filippone, Richard Mansfield, Edgard Lawson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Pages891-900
Number of pages10
DOIs
StatePublished - 2008
EventIEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008

Conference

ConferenceIEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Country/TerritoryItaly
CityPisa
Period12/15/0812/19/08

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Detecting suspicious behavior in surveillance images'. Together they form a unique fingerprint.

Cite this