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Open Access

BigBackground-Based Illumination Compensation for Surveillance Video

  • M. Ryan Bales1Email author,
  • Dana Forsthoefel1,
  • Brian Valentine1,
  • D. Scott Wills1 and
  • Linda M. Wills1
EURASIP Journal on Image and Video Processing20102011:171363

Received: 25 April 2010

Accepted: 13 December 2010

Published: 21 December 2010


Illumination changes cause challenging problems for video surveillance algorithms, as objects of interest become masked by changes in background appearance. It is desired for such algorithms to maintain a consistent perception of a scene regardless of illumination variation. This work introduces a concept we call BigBackground, which is a model for representing large, persistent scene features based on chromatic self-similarity. This model is found to comprise 50% to 90% of surveillance scenes. The large, stable regions represented by the model are used as reference points for performing illumination compensation. The presented compensation technique is demonstrated to decrease improper false-positive classification of background pixels by an average of 83% compared to the uncompensated case and by 25% to 43% compared to compensation techniques from the literature.


Pattern RecognitionComputer VisionReference PointStable RegionChallenging Problem

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Authors’ Affiliations

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA


© M. Ryan Bales et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.