Name
Abstract | ||
|---|---|---|
With the gradual opening of airspace, tracking of noncooperative low-altitude slow-speed small size (LSS) targets is important for the maintenance of security. It is still a challenging problem, especially for complex scenarios and real-time constraints. In this letter, an efficient tracking by relocalization (TRL) framework is proposed for small flying object tracking, aiming to alleviate the issue of losing moving targets in a complex background. Our designed relocalization module consists of a feature-aggregated module and a global search module. On the one hand, a feature-aggregated module is integrated into the designed framework to increase the ability to locate small targets. On the other hand, a global search module is developed to update the tracking performance, which attempts to address missed targets in long-term small object tracking tasks. What needs to be declared is that the basic tracking module cooperates with the relocalization module we designed to achieve the tracking of small targets. Performance evaluation of two small-flying target data sets and comparison with several state-of-the-art approaches demonstrate the effectiveness of the proposed framework. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2020.3043001 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Target tracking, Feature extraction, Libraries, Training, Object detection, Object tracking, Task analysis, Deep learning, low-slow-small target, target relocalization, target tracking | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingying Wang | 1 | 21 | 11.64 |
| Wei Li | 2 | 1088 | 88.08 |
| Zhanchao Huang | 3 | 0 | 0.68 |
| Ran Tao | 4 | 899 | 100.20 |
| Pengge Ma | 5 | 0 | 1.35 |