Name
Abstract | ||
|---|---|---|
•We propose a robust tracking method based on local soft cosine similarity under the Bayesian framework.•We construct the motion model of the proposed tracker by the Bayesian framework.•We propose a local soft similarity (L3SCM) and we incorporate it in the observation model of the proposed tracker.•We integrate a simple update scheme for more robustness of the proposed tracker.•We achieve comparable performances with other methods on challenging image sequences. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patrec.2018.03.026 | Pattern Recognition Letters |
Keywords | Field | DocType |
Motion analysis,Visual tracking,Soft similarity,Soft cosine measure,observation model,motion model | Computer vision,BitTorrent tracker,Trigonometric functions,Pattern recognition,Cosine similarity,Robustness (computer science),Eye tracking,Video tracking,Artificial intelligence,Vector space model,Mathematics,Bayesian probability | Journal |
Volume | ISSN | Citations |
110 | 0167-8655 | 6 |
PageRank | References | Authors |
0.42 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Driss Moujahid | 1 | 6 | 0.76 |
| Omar ElHarrouss | 2 | 22 | 6.17 |
| Hamid Tairi | 3 | 57 | 17.49 |