Name
Abstract | ||
|---|---|---|
Typical person re-identification frameworks search forkbest matches in a gallery of images that are often collected in varying conditions. The gallery usually contains image sequences for video re-identification applications. However, such a process is time consuming as video re-identification involves carrying out the matching process multiple times. In this paper, we propose a new method that extracts spatio-temporal frame sequences or tubes of moving persons and performs the re-identification in quick time. Initially, we apply a binary classifier to remove noisy images from the input query tube. In the next step, we use a key-pose detection-based query minimization technique. Finally, a hierarchical re-identification framework is proposed and used to rank the output tubes. Experiments with publicly available video re-identification datasets reveal that our framework is better than existing methods. It ranks the tubes with an average increase in the CMC accuracy of 6-8% across multiple datasets. Also, our method significantly reduces the number of false positives. A new video re-identification dataset, named Tube-based Re-identification Video Dataset (TRiViD), has been prepared with an aim to help the re-identification research community. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-020-09096-x | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Video-based Person Re-identification,Re-ranking,Person Re-identification | Journal | 79.0 |
Issue | ISSN | Citations |
33-34 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| arif ahmed | 1 | 13 | 3.98 |
| Debi Prosad Dogra | 2 | 228 | 29.89 |
| Hee-seung Choi | 3 | 87 | 5.61 |
| Seungho Chae | 4 | 12 | 6.03 |
| Ig-Jae Kim | 5 | 390 | 35.40 |
| Sk. Arif Ahmed | 6 | 0 | 0.34 |