Name
Abstract | ||
|---|---|---|
A new algorithm for background estimation and removal in video sequences obtained with stereo cameras is presented. Per-pixel Gaussian mixtures are used to model recent scene observations in the combined space of depth and luminance-invariant color. These mixture models adapt over time, and are used to build a new model of the background at each time step. This combination in itself is novel, but we also introduce the idea of modulating the learn-ing rate of the background model according to the scene activ-ity level on a per-pixel basis, so that dynamic foreground objects are incorporated into the background more slowly than are static scene changes. Our results show much greater robustness than prior state-of-the-art methods to challenging phenomena such as video displays, non-static background objects, areas of high fore-ground traffic, and similar color of foreground and background. Our method is also well-suited for use in real-time systems. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/ICIP.2001.958058 | ICIP (3) |
DocType | Citations | PageRank |
Conference | 12 | 0.80 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaile G. Gordon | 1 | 148 | 12.63 |
| J. Woodfill | 2 | 1337 | 239.65 |
| Michael Harville | 3 | 369 | 35.55 |