Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87442-3_58 | ICIC (1) |
Keywords | Field | DocType |
background intensity,complicated outdoor system,motion detection,probability density function,background modeling,foreground scene,five-dimensional feature space,traffic scene,background clutter suppression,kernel density estimation,normalized color space,kde model,kernel density estimate,feature space,color space | Computer vision,Feature vector,Color space,Motion detection,Pattern recognition,Computer science,Clutter,Artificial intelligence,Pixel,Probability density function,Variable kernel density estimation,Kernel density estimation | Conference |
Volume | ISSN | Citations |
5226 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Zhou | 1 | 0 | 0.34 |
| Zhiyuan Zeng | 2 | 6 | 4.56 |
| Jian-Zhong Zhou | 3 | 0 | 0.34 |