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

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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.

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Acknowledgements

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.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, ASRI, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul, Korea

    Kyuewang Lee & Jin Young Choi

  2. School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

    Hyung Jin Chang & Aleš Leonardis

  3. Department of Imaging Science and Arts, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Korea

    Jongwon Choi

  4. NAVER AI LAB, Green Factory, 6 Buljeong-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea

    Byeongho Heo

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  1. Kyuewang Lee
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Correspondence to Jin Young Choi.

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Lee, K., Chang, H.J., Choi, J. et al. Motion-aware ensemble of three-mode trackers for unmanned aerial vehicles. Machine Vision and Applications 32, 54 (2021). https://doi.org/10.1007/s00138-021-01181-x

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