Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel tracking method based on structured metric learning, which takes the advantages of both structured learning and distance metric learning. In our method, tracking is formulated as a structured metric learning problem, which not only considers the importance of different samples, but also improves the discriminability by learning a specific distance metric for matching. Specifically, a concrete structured metric learning method is realized by making use of the constraints from the target and its neighbour training samples under the above framework. Besides, a closed-form solution is derived for the structured metric learning problem. To improve the matching robustness, the K-nearest neighbours (KNN) distance is employed to determine the final tracking result. Experimental results in the benchmark dataset demonstrate that the proposed structured metric learning based tracking method can achieve desirable performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2950690 | IEEE ACCESS |
Keywords | DocType | Volume |
Target tracking, Learning systems, Training, Deep learning, Robustness, Object tracking, structured metric learning, KNN distance | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolin Zhao | 1 | 57 | 14.22 |
| Zhuofan Xu | 2 | 0 | 0.34 |
| Boxin Zhao | 3 | 0 | 2.70 |
| Xiaolong Chen | 4 | 0 | 0.34 |
| Zongzhe Li | 5 | 0 | 0.34 |