Name
Abstract | ||
|---|---|---|
•Propose structured and weighted multi-task low rank tracker with novel task definition.•Weighted nuclear norm adaptively assigns different tracking importance on different rank components of multiple tasks, and avoids over-shrink.•Take advantage of the local and global multi-task tracking modals simultaneously, and mine their structure information.•Simultaneously learn and update the adaptively discriminative subspace and classifier.•The developed tracker is a general model for most existing multi-task trackers. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patcog.2018.04.002 | Pattern Recognition |
Keywords | Field | DocType |
Robust multi-subtask learning,Structured and weighted low rank,Group-sparsity regularization,Normalized collaboration metric | BitTorrent tracker,Pattern recognition,Subspace topology,Robustness (computer science),Matrix norm,Video tracking,Regularization (mathematics),Artificial intelligence,Classifier (linguistics),Discriminative model,Mathematics | Journal |
Volume | Issue | ISSN |
81 | 1 | 0031-3203 |
Citations | PageRank | References |
1 | 0.35 | 47 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baojie Fan | 1 | 41 | 10.48 |
| Xiaomao Li | 2 | 7 | 3.61 |
| Yang Cong | 3 | 684 | 38.22 |
| Y. Tang | 4 | 243 | 33.69 |