Name
Abstract | ||
|---|---|---|
We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TIP.2012.2205700 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
adaptive discriminative object representation,target tracking,binary classification problem,partial least squares analysis,low dimensional discriminative feature subspace,appearance model,least squares approximations,object tracking algorithm,object tracking,ground truth appearance information,object appearance correlation | Computer vision,Pattern recognition,Subspace topology,Binary classification,Partial least squares regression,Active appearance model,Video tracking,Ground truth,Correlation,Artificial intelligence,Discriminative model,Mathematics | Journal |
Volume | Issue | ISSN |
21 | 10 | 1941-0042 |
Citations | PageRank | References |
52 | 1.32 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qing Wang | 1 | 239 | 9.21 |
| Feng Chen | 2 | 431 | 33.92 |
| Wenli Xu | 3 | 1327 | 63.69 |
| Yang Ming-Hsuan | 4 | 15303 | 620.69 |