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.
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Acknowledgments
This material is based upon work supported in part by the U. S. Army Research Laboratory and the U. S. Department of Defense under grant number W911NF-15-1-0024, W911NF-15-1-0455, and W911NF-16-1-0473. This support does not necessarily imply endorsement by the DoD or ARL.
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Porr, W., Easton, J., Tavakkoli, A., Loffredo, D., Simmons, S. (2018). Accurate and Efficient Non-Parametric Background Detection for Video Surveillance. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_9
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DOI: https://doi.org/10.1007/978-3-030-03801-4_9
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