Robust real-time unusual event detection using multiple fixed-location monitors

Amit Adam, Ehud Rivlin, Ilan Shimshoni, Daviv Reinitz

Research output: Contribution to journalArticlepeer-review

Abstract

We present a novel algorithm for detection of certain types of unusual events. The algorithm is based on multiple local monitors which collect low-level statistics. Each local monitor produces an alert if its current measurement is unusual, and these alerts are integrated to a final decision regarding the existence of an unusual event. Our algorithm satisfies a set of requirements that are critical for successful deployment of any large-scale surveillance system. In particular it requires a minimal setup (taking only a few minutes) and is fully automatic afterwards. Since it is not based on objects' tracks, it is robust and works well in crowded scenes where tracking-based algorithms are likely to fail. The algorithm is effective as soon as sufficeint low-level observations representing the routine activity have been collected, which usually happens after a few minutes. Our algorithm runs in realtime. It was tested on a variety of real-life crowded scenes. A ground-truth was extracted for these scenes, with respect to which detection and false-alarm rates are reported.

Original languageEnglish
Pages (from-to)555-560
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume30
Issue number3
DOIs
StatePublished - Mar 2008

Keywords

  • Unusual events
  • Video analysis
  • Video surveillance

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics

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