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Proceedings Paper

Detecting background changes in environments with dynamic foreground by separating probability distribution function mixtures using Pearson's method of moments
Author(s): Colleen Jenkins; Jay Jordan; Jeff Carlson
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Paper Abstract

This paper presents parameter estimation techniques useful for detecting background changes in a video sequence with extreme foreground activity. A specific application of interest is automated detection of the covert placement of threats (e.g., a briefcase bomb) inside crowded public facilities. We propose that a histogram of pixel intensity acquired from a fixed mounted camera over time for a series of images will be a mixture of two Gaussian functions: the foreground probability distribution function and background probability distribution function. We will use Pearson's Method of Moments to separate the two probability distribution functions. The background function can then be "remembered" and changes in the background can be detected. Subsequent comparisons of background estimates are used to detect changes. Changes are flagged to alert security forces to the presence and location of potential threats. Results are presented that indicate the significant potential for robust parameter estimation techniques as applied to video surveillance.

Paper Details

Date Published: 27 February 2007
PDF: 7 pages
Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 64970X (27 February 2007); doi: 10.1117/12.704215
Show Author Affiliations
Colleen Jenkins, New Mexico State Univ. (United States)
Jay Jordan, New Mexico State Univ. (United States)
Jeff Carlson, Sandia National Labs. Albuquerque (United States)

Published in SPIE Proceedings Vol. 6497:
Image Processing: Algorithms and Systems V
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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