Name
Title | ||
|---|---|---|
Extended Fuzzy Background Modeling For Moving Vehicle Detection Using Infrared Vision |
Abstract | ||
|---|---|---|
Running average is a simple and effective background modeling method that generates adaptive background image for moving object detection. Fuzzy Running Average (FRA) improves the selectivity of Standard Running Average (SRA). However, its background restoration rate is slow. This leads to false object detection when a static object becomes dynamic. To overcome this problem, an Extended Fuzzy Running Average (EFRA) is proposed. The results show that the EFRA not only retains the selectivity benefit of FRA, but also improves the restoration rate significantly. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1587/elex.8.340 | IEICE ELECTRONICS EXPRESS |
Keywords | Field | DocType |
fuzzy background modeling, vehicle detection, thermograph | Object detection,Computer vision,Moving vehicle,Computer science,Fuzzy logic,Vehicle detection,Infrared vision,Artificial intelligence,Moving average | Journal |
Volume | Issue | ISSN |
8 | 6 | 1349-2543 |
Citations | PageRank | References |
3 | 0.59 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boon Chin Yeo | 1 | 3 | 0.59 |
| Way-soong Lim | 2 | 19 | 3.68 |
| Heng-Siong Lim | 3 | 45 | 9.65 |
| Wai Kit Wong | 4 | 394 | 20.10 |