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Performance evaluation of low resolution visual tracking for unmanned aerial vehicles

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Abstract

Several datasets for unmanned aerial vehicle (UAV) visual tracking research have been released in recent years. Despite their usefulness, whether they are sufficient for understanding the strengths and weakness of different resolution videos tracking remains questionable. Tracking in low resolution videos is a critical problem in UAV tracking. To address this issue, we construct a group of low resolution tracking datasets and study the performance of different trackers on these datasets. We find that some trackers suffered more performance degradation than others, which brings to light a previously unexplored aspect of the tracking methods. The relative rank of these trackers based on their tracking results on the datasets may change in the presence of low resolution. Based on these findings, we develop a multiple feature tracking framework which takes advantage of image enhancement scheme to improve image quality. In addition, we utilize the forward and backward tracking to evaluate multiple feature tracking results. Experimental results demonstrate that our tracker is competitive in performance to state-of-the-art methods in different resolutions scenarios. We believe our studies can provide a solid baseline when conducting experiments for low resolution UAV tracking research.

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Acknowledgement

This work was partially supported by National Science Found for Young Scholars under Grant No. 61806186, State Key Laboratory of Robotics and System (HIT) under Grant No. SKLRS-2019-KF-15, the program ‘Construction of Fujian Research Institute on Intelligent Logistics Industry Technology’ under Grant No. 2018H2001, CAS Pioneer Hundred Talents Program (Type C) under Grant No. 2017-122, and the program ‘Quanzhou Science and Technology Plan’ under Grant No. 2019C112, No. 2019C011R and No. 2019STS08.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Sun Yat-Sen University, Guangzhou, China

    Yong Wang

  2. Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, China

    Xian Wei

  3. Fortiss GmbH, Technical University of Munich, Munich, Germany

    Hao Shen

  4. Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Jilin Hu

  5. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Lingkun Luo

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  1. Yong Wang
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  2. Xian Wei
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  3. Hao Shen
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  4. Jilin Hu
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  5. Lingkun Luo
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Correspondence to Xian Wei.

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Wang, Y., Wei, X., Shen, H. et al. Performance evaluation of low resolution visual tracking for unmanned aerial vehicles. Neural Comput & Applic 33, 2229–2248 (2021). https://doi.org/10.1007/s00521-020-05067-3

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  • DOI: https://doi.org/10.1007/s00521-020-05067-3

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