Name
Abstract | ||
|---|---|---|
Vehicle re-identification (Re-ID) technology plays an important role in the intelligent transportation system for smart city. Due to various uncertain factors in the real-world scenarios, (e.g., resolution variation, viewpoint variation, illumination changes, occlusion, etc., vehicle Re-ID is a very challenging task. To resist the adverse effect of resolution variation, a joint pyramid feature representation network (JPFRN) for vehicle Re-ID is proposed in this paper. Based on the consideration that various convolution blocks with different depths hold different resolutions and semantic information of the vehicle image, the proposed JPFRN method employs a base network to obtain multi-resolution vehicle features in the first stage. Then, a pyramid feature representation scheme is developed to reconstruct and integrate the obtained multi-resolution vehicle features together. Finally, these pyramid features are jointly represented for learning a more discriminative feature under the supervision of joint Triplet loss and softmax loss. Extensive experimental results on two commonly-used vehicle databases (i.e., VehicleID and VeRi) show that the proposed JPFRN is superior to multiple recently-developed vehicle Re-ID methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11036-020-01561-z | MOBILE NETWORKS & APPLICATIONS |
Keywords | DocType | Volume |
Internet of Things, Intelligent transport system, Vehicle re-identification, Joint pyramid feature representation, Deep learning | Journal | 25 |
Issue | ISSN | Citations |
5 | 1383-469X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangwei Lin | 1 | 0 | 0.34 |
| Huanqiang Zeng | 2 | 395 | 36.94 |
| Jinhui Hou | 3 | 6 | 1.81 |
| Jiuwen Cao | 4 | 178 | 18.99 |
| Jianqing Zhu | 5 | 78 | 10.10 |
| Jing Chen | 6 | 88 | 10.64 |