Motion-aware ensemble of three-mode trackers for unmanned aerial vehicles

Kyuewang Lee, Hyung Jin Chang, Jongwon Choi, Byeongho Heo, Aleš Leonardis, Jin Young Choi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
16 Downloads (Pure)


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.

Original languageEnglish
Article number54
JournalMachine Vision and Applications
Issue number3
Early online date4 Mar 2021
Publication statusPublished - May 2021

Bibliographical note

Funding 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.

Publisher Copyright:
© 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

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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