Name
Abstract | ||
|---|---|---|
Multitask and low-rank learning methods have attracted increasing attention for visual tracking. However, most trackers only focus on learning appearance subspace basis or the sparse low rankness of representation and, thus, do not make full use of the structure information among and inside target candidates (or samples). In this paper, we propose a dual-graph regularized discriminative low-rank l... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMM.2018.2804762 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Target tracking,Task analysis,Visualization,Robustness,Adaptation models | BitTorrent tracker,Pattern recognition,Subspace topology,Visualization,Computer science,Robustness (computer science),Dual graph,Eye tracking,Artificial intelligence,Classifier (linguistics),Discriminative model | Journal |
Volume | Issue | ISSN |
20 | 9 | 1520-9210 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baojie Fan | 1 | 41 | 10.48 |
| Yang Cong | 2 | 684 | 38.22 |
| Y. Tang | 3 | 243 | 33.69 |