Application of Non-Parametric Kernel Density Background Modeling Method in Intelligent Video Surveillance System

Ying Zhang; Meng Xin Li; Jing Hou

doi:10.4028/www.scientific.net/AMM.799-800.1117

Paper Titles

A Nonlinear Friction Model for an Electromechanical Actuator Valve
p.1096

Autonomous Area Mapping with Low Cost Hovercraft Platform
p.1102

Knowledge Push Technology for Complex Mechatronic Products Design Based on Ontology and Variable Precision Rough Set
p.1107

Effect of Flow Characteristics on the Operation of a Solenoid Switching Control Valve
p.1113

Application of Non-Parametric Kernel Density Background Modeling Method in Intelligent Video Surveillance System
p.1117

Optimal Control for the Fuser System of Electro-Photographic Printer
p.1121

PLC-Based Embedded Intelligent Control Research
p.1127

The Second Lyapunov Function Method in Construction of Control Systems with the Increased Potential of Robust Stability in the Class of Catastrophe “Hyperbolic Umbilic”
p.1132

Design of Control System in the Class of Two-Parametric Structurally Stable Mappings
p.1137

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 799-800Application of Non-Parametric Kernel Density...

Application of Non-Parametric Kernel Density Background Modeling Method in Intelligent Video Surveillance System

Abstract:

Aiming to get better real time performance and effect of detection in dynamic scene of intelligent video surveillance system, non-parametric kernel density estimation (KDE) is used to model the background. And to solve the foreground detection is not precise enough, background subtraction method is fused to detect the foreground. And some modified work is done to suppress shadow and noise. Experiments show that the method proposed can get better real time performance and low noise detect result in intelligent video surveillance system.

You might also be interested in these eBooks

Mechanical and Electrical Technology VII

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 799-800)

Pages:

1117-1120

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.799-800.1117

Citation:

Cite this paper

Online since:

October 2015

Authors:

Ying Zhang*, Meng Xin Li, Jing Hou

Keywords:

Intelligent Video Surveillance System, Non-Parametric Kernel Density, Object Dtection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Friedman N，Russell S. Image segmentation in video sequences：a probabilistic approach[C]. Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence（UAI）. San Francisco, USA: [s. n. ], (1997).

Google Scholar

[2] C. Stauffer, E. Grimson. Learning patterns of activity using real-time tracking[J]. IEEE Transactions on Pattern Recognition Ma-chine Intelligence, 2000, 22(8): 747-757.

DOI: 10.1109/34.868677

Google Scholar

[3] A. Elgammal, R. Duraiswami, D. Harwood. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance [J]. Proceedings of the IEEE, 2002, 90(7): 1151-1163.

DOI: 10.1109/jproc.2002.801448

Google Scholar

[4] Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction[C]. Proceedings of 6th European Conference on Computer Vision. Dublin，Ireland: [s. n. ], 2000: 751-767.

DOI: 10.1007/3-540-45053-x_48

Google Scholar

[5] LIU Di, GAO Meifeng. Foreground object detection based on background subtraction image kernel density estimation [J]. Computer Engineering and Applications, 2013, 49(6): 170-174.

Google Scholar

[6] E.C. Zhou, C.P. Liu, L.Y. Zhang et. al. Foreground object detection based on time information window adaptive kernel density estimation [J]. Journal on Communications, 2011, 32(3): 106-114.

Google Scholar