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Joint Pyramid Feature Representation Network for Vehicle Re-identification.
- Source :
- Mobile Networks & Applications; Oct2020, Vol. 25 Issue 5, p1781-1792, 12p
- Publication Year :
- 2020
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- INTELLIGENT transportation systems
SMART cities
Subjects
Details
- Language :
- English
- ISSN :
- 1383469X
- Volume :
- 25
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Mobile Networks & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 146325173
- Full Text :
- https://doi.org/10.1007/s11036-020-01561-z