Name
Abstract | ||
|---|---|---|
We propose an adaptive model for backgrounds containing significant stochastic motion (e.g. water). The new model is based on a generalization of the Stauffer–Grimson background model, where each mixture component is modeled as a dynamic texture. We derive an online K-means algorithm for updating the parameters using a set of sufficient statistics of the model. Finally, we report on experimental results, which show that the proposed background model both quantitatively and qualitatively outperforms state-of-the-art methods in scenes containing significant background motions. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s00138-010-0262-3 | Mach. Vis. Appl. |
Keywords | Field | DocType |
Dynamic textures,Background models,Background subtraction,Mixture models,Adaptive models | Background subtraction,Computer vision,Pattern recognition,MIXTURE COMPONENT,Computer science,Artificial intelligence,Sufficient statistic,Mixture model | Journal |
Volume | Issue | ISSN |
22 | 5 | 0932-8092 |
Citations | PageRank | References |
34 | 1.15 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antoni B. Chan | 1 | 1658 | 88.09 |
| Vijay Mahadevan | 2 | 1063 | 35.39 |
| Nuno Vasconcelos | 3 | 5410 | 273.99 |