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BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision

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Abstract

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone—the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack Mayer et al. in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 13444–13454, 2021) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com.

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Availability of data and materials.

All data will be made available on reasonable request.

Notes

  1. http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html.

  2. https://votchallenge.net/.

  3. http://got-10k.aitestunion.com.

  4. https://cis.temple.edu/lasot/.

  5. http://videocube.aitestunion.com.

  6. https://lafi.github.io/LPN/.

  7. https://captain-whu.github.io/DOTA/.

  8. https://cemse.kaust.edu.sa/ivul/uav123.

  9. https://www.unrealengine.com.

  10. https://github.com/flyers/drone-tracking.

  11. https://sites.google.com/site/daviddo0323/projects/uavdt.

  12. https://github.com/VisDrone/VisDrone-Dataset.

  13. https://sites.google.com/view/elizabethbondi/dataset.

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Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

    Xin Zhao

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Xin Zhao, Shiyu Hu & Jing Zhang

  3. School of Instrument Science and Engineering, Southeast University, Nanjing, China

    Yipei Wang

  4. Suzhou Institute of Nano-tech and Nano-bionics, Chinese Academy of Sciences, Suzhou, China

    Yimin Hu, Rongshuai Liu, Renshu Li, Kun Liu & Jiadong Li

  5. Department of Computer Science, Stony Brook University, Stony Brook, USA

    Haibin Ling

  6. University of Wisconsin-Madison, Madison, USA

    Yin Li

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Zhao, X., Hu, S., Wang, Y. et al. BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision. Int J Comput Vis 132, 1659–1684 (2024). https://doi.org/10.1007/s11263-023-01937-0

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