Name
Abstract | ||
|---|---|---|
Person reidentification (re-id) suffers from a challenging issue due to the significant inconsistency of the camera network, including position, view, and brands. In this paper, we propose a deep camera-aware metric learning (DCAML) model, where images on the identity-level spaces are further projected into different camera-level subspaces, which can explore the inherent relationship between identity and camera. Furthermore, we exploit dynamic training strategy to jointly multiple metrics for identity-camera relationship learning and thus consumedly elevating the retrieval accuracy. Extensive experiments on the three public datasets demonstrated that our method performs competitive results compared to the state-of-the-art person re-id methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1155/2021/8859088 | WIRELESS COMMUNICATIONS & MOBILE COMPUTING |
DocType | Volume | ISSN |
Journal | 2021 | 1530-8669 |
Citations | PageRank | References |
0 | 0.34 | 21 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Liu | 1 | 0 | 0.34 |
| Ping Liang | 2 | 0 | 0.34 |
| Lei Liu | 3 | 0 | 0.34 |
| Zhiqiang Hao | 4 | 0 | 1.35 |
| Xin Xu | 5 | 162 | 40.08 |