Name
Abstract | ||
|---|---|---|
In order to realize robust visual tracking in natural environments, a novel algorithm based on adaptive appearance model is proposed. The model can adapt to changes in object appearance over time. A mixture of three Gaussian distributions models the value of each pixel. An online Expectation Maximization (EM) algorithm is developed to update the parameters of the Gaussians. The observation model in the particle filter is designed based on the adaptive appearance model. Numerous experimental results demonstrate that our proposed algorithm can track objects well under illumination change, large pose variation, and partial or full occlusion. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881223_64 | ICNC (2) |
Keywords | Field | DocType |
full occlusion,illumination change,natural environment,observation model,gaussian distributions model,numerous experimental result,novel algorithm,robust object tracking algorithm,proposed algorithm,object appearance,adaptive appearance model,visual tracking,gaussian distribution,expectation maximization,em algorithm,object tracking,particle filter | Approximation algorithm,Computer vision,Expectation–maximization algorithm,Computer science,Particle filter,Active appearance model,Gaussian process,Pixel,Artificial intelligence,Adaptive algorithm,Motion estimation,Machine learning | Conference |
Volume | ISSN | ISBN |
4222 | 0302-9743 | 3-540-45907-3 |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiqiang Hu | 1 | 56 | 6.96 |
| Guozhuang Liang | 2 | 1 | 0.70 |
| Zhongliang Jing | 3 | 351 | 39.38 |