Paper
27 February 2007 Detecting background changes in environments with dynamic foreground by separating probability distribution function mixtures using Pearson's method of moments
Colleen Jenkins, Jay Jordan, Jeff Carlson
Author Affiliations +
Proceedings Volume 6497, Image Processing: Algorithms and Systems V; 64970X (2007) https://doi.org/10.1117/12.704215
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States
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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Colleen Jenkins, Jay Jordan, and Jeff Carlson "Detecting background changes in environments with dynamic foreground by separating probability distribution function mixtures using Pearson's method of moments", Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 64970X (27 February 2007); https://doi.org/10.1117/12.704215
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CITATIONS
Cited by 1 scholarly publication.
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KEYWORDS
Video surveillance

Video

Cameras

Environmental sensing

Surveillance

Data modeling

Frame grabbers

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