Abstract
Background modeling is an important component of visual surveillance system. In some complicated outdoor system, such as traffic scene in night, solutions to problems as illumination and shadow disturbance are provided. The kernel density estimation is exploited to estimate the probability density function of background intensity and then to classify the pixel into background or foreground scene. Toward the modeling of dynamic characteristics, a normalized color space is proposed as part of a five-dimensional feature space. And experiment demonstrates the performance of the proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wren, C.R., Azarbayejani, A., Darrell, T.J., Pentland, A.P.: Pfinder: Real-time Tracking of the Human Body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 780–785 (1997)
Friedman, N., Russell, S.: Image Segmentation in Video Sequences: A Probabilistic Approach. In: Thirteenth Conference on Uncertainty in Artificial Intelligence(UAI) (1997)
Grimson, W.E.L., Stauffer, C., Romano, R., Lee, L.: Using Adaptive Tracking to Classify and Monitor Activities in a Site. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, Santa Barbara, CA (1998)
Mittal, A., Paragios, N.: Motion-based Background Subtraction Using Adaptive Kernel Density Estimation. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2004)
Sheikh, Y.: Bayesian Modeling of Dynamic Scenes for Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(11) (2005)
Elgammal, A., Duraiswami, R., Davis, L.: Probabilistic Tracking in Joint Feature-Spatial Spaces. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2003)
Elgammal, A., Harwood, D.: Non-parametric Model for Background Subtraction. In: Proc. European Conference on Computer Vision (2000)
Elgammal, A., Harwood, D., Davis, L.: Background and Foreground Modeling Using Non-Parametric Kernel Density Estimation for Visual Surveillance. In: Proc. IEEE (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, H., Zeng, Zy., Zhou, Jz. (2008). Motion Detection with Background Clutter Suppression Based on KDE Model. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_58
Download citation
DOI: https://doi.org/10.1007/978-3-540-87442-3_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87440-9
Online ISBN: 978-3-540-87442-3
eBook Packages: Computer ScienceComputer Science (R0)