Name
Abstract | ||
|---|---|---|
Person Re-Identification (ReID) refers to the task of verifying the identity of a pedestrian observed from non-overlapping surveillance cameras views. Recently, it has been validated that re-ranking could bring extra performance improvements in person ReID. However, the current re-ranking approaches either require feedbacks from users or suffer from burdensome computation cost. In this paper, we propose to exploit a density-adaptive kernel technique to perform efficient and effective re-ranking for person ReID. Specifically, we present two simple yet effective re-ranking methods, termed inverse Density-Adaptive Kernel based Re-ranking (inv-DAKR) and bidirectional Density-Adaptive Kernel based Re-ranking (bi-DAKR), which are based on a smooth kernel function with a density-adaptive parameter. Experiments on six benchmark data sets confirm that our proposals are effective and efficient. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8545619 | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
DocType | Volume | ISSN |
Conference | abs/1805.07698 | 1051-4651 |
Citations | PageRank | References |
0 | 0.34 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruo-Pei Guo | 1 | 2 | 1.78 |
| Chunguang Li | 2 | 748 | 63.37 |
| Li Yonghua | 3 | 3 | 3.53 |
| Jiaru Lin | 4 | 646 | 80.74 |