Name
Abstract | ||
|---|---|---|
Detection of moving objects in image sequence is a fundamental step of information extraction in many vision applications such as visual surveillance, people tracking, traffic monitoring. Many background models have been introduced to deal with different problems. Gaussian mixture model is considered to be one of the most successful solutions. It is a robust and stable method for background subtraction. It can efficiently deal with multimodal distributions caused by shadows, swaying trees and other knotty problems of the real world. However, the method suffers from foreground objects bending into the background too fast. In addition, it can not deal with the problem of slow-moving objects. In this paper, an efficient method is presented to deal with the problem through improvement on the background updating period using different learning rates for the estimated background and foreground pixels. The experiment result shows the method works better than the typical Gaussian mixture model. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23881-9_23 | AICI (1) |
Keywords | Field | DocType |
foreground pixel,gaussian mixture model,stable method,background subtraction,object detection,efficient method,foreground object,different problem,different learning rate,improved gaussian mixture model,estimated background,background model | Background subtraction,Object detection,Computer vision,Pattern recognition,Computer science,Information extraction,Artificial intelligence,Pixel,Image sequence,Visual surveillance,Machine learning,Mixture model | Conference |
Volume | ISSN | Citations |
7002 | 0302-9743 | 4 |
PageRank | References | Authors |
0.46 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Chen | 1 | 712 | 75.60 |
| Zhezhou Yu | 2 | 22 | 5.50 |
| Qing Wen | 3 | 4 | 0.46 |
| Yangquan Yu | 4 | 4 | 0.46 |