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Published in:
2018 | OriginalPaper | Chapter
Accurate and Efficient Non-Parametric Background Detection for Video Surveillance
Authors : William Porr, James Easton, Alireza Tavakkoli, Donald Loffredo, Sean Simmons
Published in: Advances in Visual Computing
Publisher: Springer International Publishing
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Abstract
In this paper, we propose an adaptive, non-parametric method of separating background from foreground in static camera video feed. Our algorithm processes each frame pixel-wise, and calculates a probability density function at each location using previously observed values at that location. This method makes several improvements over the traditional kernel density estimation model, accomplished through applying a dynamic learning weight to observed intensity values in the function, consequentially eradicating the large computational and memory load often associated with non-parametric techniques. In addition, we propose a novel approach to the classic background segmentation issue of “ghosting” by exploiting the spatial relationships among pixels.