BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for
  Robust Vision
Xin Zhao Shiyu Hu Yipei Wang Jing Zhang Yimin Hu Rongshuai Liu Haibin Ling Yin Li Renshu Li Kun Liu Jiadong Li
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 KeepTrack) 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.