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  • Research Article
  • Open Access

Spatio-temporal Background Models for Outdoor Surveillance

EURASIP Journal on Advances in Signal Processing20052005:101240

https://doi.org/10.1155/ASP.2005.2281

  • Received: 2 January 2004
  • Published:

Abstract

Video surveillance in outdoor areas is hampered by consistent background motion which defeats systems that use motion to identify intruders. While algorithms exist for masking out regions with motion, a better approach is to develop a statistical model of the typical dynamic video appearance. This allows the detection of potential intruders even in front of trees and grass waving in the wind, waves across a lake, or cars moving past. In this paper we present a general framework for the identification of anomalies in video, and a comparison of statistical models that characterize the local video dynamics at each pixel neighborhood. A real-time implementation of these algorithms runs on an 800 MHz laptop, and we present qualitative results in many application domains.

Keywords and phrases:

  • anomaly detection
  • dynamic backgrounds
  • spatio-temporal image processing
  • background subtraction
  • real-time application

Authors’ Affiliations

(1)
Department of Computer Science and Engineering, Washington University in St. Louis, MO 63130, USA

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