Name
Abstract | ||
|---|---|---|
AbstractThe task of person re-identification (re-ID) is to find the same pedestrian across non-overlapping camera views. Generally, the performance of person re-ID can be affected by background clutter. However, existing segmentation algorithms cannot obtain perfect foreground masks to cover the background information clearly. In addition, if the background is completely removed, some discriminative ID-related cues (i.e., backpack or companion) may be lost. In this article, we design a dual-stream network consisting of a Provider Stream (P-Stream) and a Receiver Stream (R-Stream). The R-Stream performs an a priori optimization operation on foreground information. The P-Stream acts as a pusher to guide the R-Stream to concentrate on foreground information and some useful ID-related cues in the background. The proposed dual-stream network can make full use of the a priori optimization and guided-learning strategy to learn encouraging foreground information and some useful ID-related information in the background. Our method achieves Rank-1 accuracy of 95.4% on Market-1501, 89.0% on DukeMTMC-reID, 78.9% on CUHK03 (labeled), and 75.4% on CUHK03 (detected), outperforming state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3447715 | ACM Transactions on Multimedia Computing, Communications, and Applications |
Keywords | DocType | Volume |
Person re-identification, a priori optimization, guided-learning, foreground images | Journal | 17 |
Issue | ISSN | Citations |
4 | 1551-6857 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junyi Wu | 1 | 2 | 1.74 |
| Yan Huang | 2 | 28 | 5.76 |
| Qiang Wu | 3 | 304 | 40.42 |
| Zhipeng Gao | 4 | 100 | 31.83 |
| Jianqiang Zhao | 5 | 0 | 0.34 |
| Liqin Huang | 6 | 10 | 2.31 |