Name
Abstract | ||
|---|---|---|
Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a three- component system for background maintenance: the pixel- level component performs Wiener filtering to make probabilistic predictions of the expected background; the region-level component fills in homogeneous regions of foreground objects; and the frame-level component detects sudden, global changes in the image and swaps in better approximations of the background. We compare our system with 8 other background subtraction algorithms. Wallflower is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur. Finally, we analyze the experimental results and propose normative principles for background maintenance. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/ICCV.1999.791228 | ICCV |
Keywords | Field | DocType |
wiener filter,statistics,motion estimation,switches,pixel,layout,lighting,global change,background subtraction | Wiener filter,Background subtraction,Wallflower,Computer vision,Computer science,Homogeneous,Foreground detection,Artificial intelligence,Probabilistic logic,Motion estimation | Conference |
Volume | Issue | Citations |
1 | 1 | 812 |
PageRank | References | Authors |
68.05 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kentaro Toyama | 1 | 4296 | 347.17 |
| John Krumm | 2 | 3954 | 355.60 |
| Barry Brumitt | 3 | 1609 | 203.91 |
| Brian Meyers | 4 | 2116 | 182.22 |