Name
Abstract | ||
|---|---|---|
Vehicle re-identification (Re-ID) has been attracting more interests in computer vision owing to its great contributions in urban surveillance and intelligent transportation. With the development of deep learning approaches, vehicle Re-ID still faces a near-duplicate challenge, which is to distinguish different instances with nearly identical appearances. Previous methods simply rely on the global visual features to handle this problem. In this paper, we proposed a simple but efficient part-regularized discriminative feature preserving method which enhances the perceptive ability of subtle discrepancies. We further develop a novel framework to integrate part constrains with the global Re-ID modules by introducing an detection branch. Our framework is trained end-to-end with combined local and global constrains. Specially, without the part-regularized local constrains in inference step, our Re-ID network outperforms the state-of-the-art method by a large margin on large benchmark datasets VehicleID and VeRi-776. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.00412 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Computer science,Artificial intelligence | Conference | 1063-6919 |
Citations | PageRank | References |
8 | 0.46 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bing He | 1 | 8 | 0.46 |
| Jia Li | 2 | 524 | 42.09 |
| Y. Zhao | 3 | 277 | 33.44 |
| Yonghong Tian | 4 | 1057 | 102.81 |