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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barnich, O., Droogenbroeck, M.V.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011). https://doi.org/10.1109/TIP.2010.2101613
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90(7), 1151–1163 (2002). https://doi.org/10.1109/JPROC.2002.801448
Hati, K.K., Sa, P.K., Majhi, B.: Intensity range based background subtraction for effective object detection. IEEE Signal Process. Lett. 20(8), 759–762 (2013)
Lu, X.: A multiscale spatio-temporal bakcground model for motion detection. In: IEEE International Conference on Image Processing (ICIP) (2014)
Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17(7), 1168–1177 (2008). https://doi.org/10.1109/TIP.2008.924285
Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17(7), 1168–1177 (2008)
Manzanera, A.: \({\sigma }-{\delta }\) background subtraction and the zipf law. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 42–51. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76725-1_5
Min, J.: Multicore goes mainstream (2014). http://embedded-computing.com/articles/multicore-goes-mainstream/
Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics (2005)
Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: Proceedings IEEE Workshop on Change Detection (CDW-2014) at CVPR-2014 (2014)
Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR) (2004)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-03801-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03800-7
Online ISBN: 978-3-030-03801-4
eBook Packages: Computer ScienceComputer Science (R0)