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.
Bibliographical noteFunding Information:
This work was supported by Next-Generation ICD program through NRF funded by Ministry of S&ICT [2017M3C4A7077582] and ICT R&D Program MSIP/IITP [2017-0-00306, Outdoor Surveillance Robots], and BK21 4th program.
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
- Correlation filter tracking
- Motion-aware ensemble method
- Unmanned surveillance vehicles
- Visual tracking
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Computer Science Applications