Name
Abstract | ||
|---|---|---|
To tackle problems arising from unexpected camera motions in unmanned aerial vehicles (UAVs), we propose a three-mode ensemble tracker where each mode specializes in distinctive situations. The proposed ensemble tracker is composed of appearance-based tracking mode, homography-based tracking mode, and momentum-based tracking mode. The appearance-based tracking mode tracks a moving object well when the UAV is nearly stopped, whereas the homography-based tracking mode shows good tracking performance under smooth UAV or object motion. The momentum-based tracking mode copes with large or abrupt motion of either the UAV or the object. We evaluate the proposed tracking scheme on a widely-used UAV123 benchmark dataset. The proposed motion-aware ensemble shows a 5.3% improvement in average precision compared to the baseline correlation filter tracker, which effectively employs deep features while achieving a tracking speed of at least 80fps in our experimental settings. In addition, the proposed method outperforms existing real-time correlation filter trackers. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s00138-021-01181-x | MACHINE VISION AND APPLICATIONS |
Keywords | DocType | Volume |
Visual tracking, Correlation filter tracking, Motion-aware ensemble method, Unmanned surveillance vehicles | Journal | 32 |
Issue | ISSN | Citations |
3 | 0932-8092 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyuewang Lee | 1 | 16 | 1.21 |
| Hyung Jin Chang | 2 | 301 | 22.83 |
| Jongwon Choi | 3 | 14 | 5.92 |
| Byeongho Heo | 4 | 40 | 7.28 |
| Ales Leonardis | 5 | 1636 | 147.33 |
| Jin Young Choi | 6 | 768 | 99.57 |