Name
Abstract | ||
|---|---|---|
A mixture of Gaussians is a common model for background subtraction that has been used in many applications. However modelling background pixels using this model results into a low-level process at pixel level. Its advantages are: adaptive to background changes; robust and stable; can cope with many common problems; works very well for fast moving objects in complex environments. Some of its main drawbacks are: a subtracted (moving object) region may contain holes; it can not solve partial occlusion problems, and it requires updates in cases of shadows or sudden changes in the scene. We present a multi-layered mixture of Gaussians model named PixelCodes. We combine the mixture of Gaussians model with concepts defined by region level and frame level considerations. Our experimental results show that our method improved the accuracy of extracting moving objects from background. A single stationary camera lots been used. |
Year | Venue | Keywords |
|---|---|---|
2004 | CISST '04: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON IMAGING SCIENCE, SYSTEMS, AND TECHNOLOGY | video sequence analysis, surveillance systems, background subtraction, Gaussian mixture models |
Field | DocType | Citations |
Background subtraction,Pattern recognition,Computer science,Artificial intelligence,Mixture model | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Zang | 1 | 26 | 4.66 |
| Reinhard Klette | 2 | 1743 | 228.94 |