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Spatio-temporal Background Models for Outdoor Surveillance

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.

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Correspondence to Robert Pless.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Pless, R. Spatio-temporal Background Models for Outdoor Surveillance. EURASIP J. Adv. Signal Process. 2005, 101240 (2005). https://doi.org/10.1155/ASP.2005.2281

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Keywords and phrases:

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