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Open Access

Spatio-temporal Background Models for Outdoor Surveillance

EURASIP Journal on Advances in Signal Processing20052005:101240

Received: 2 January 2004

Published: 25 August 2005


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 detectiondynamic backgroundsspatio-temporal image processingbackground subtractionreal-time application

Authors’ Affiliations

Department of Computer Science and Engineering, Washington University in St. Louis, USA


© Pless 2005