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BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision
Xin Zhao1,2; Shiyu Hu2; Yipei Wang3; Zhang Jing2; Yimin Hu4; Rongshuai Liu4; Haibin Ling5; Yin Li6; Renshu Li4; Kun Liu4; Jiadong Li4
Source PublicationInternational Journal of Computer Vision
2024
Volume132Pages:1659-1684
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.

Indexed BySCI
Sub direction classification无人系统
planning direction of the national heavy laboratoryAI For Science
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57469
Collection复杂系统认知与决策实验室_智能系统与工程
Corresponding AuthorShiyu Hu
Affiliation1.University of Science and Technology Beijing
2.Institute of Automation, Chinese Academy of Sciences
3.School of Instrument Science and Engineering, Southeast University,
4.Suzhou Institute of Nano-tech and Nano-bionics, Chinese Academy of Sciences
5.Department of Computer Science, Stony Brook University
6.University of Wisconsin-Madison
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xin Zhao,Shiyu Hu,Yipei Wang,et al. BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision[J]. International Journal of Computer Vision,2024,132:1659-1684.
APA Xin Zhao.,Shiyu Hu.,Yipei Wang.,Zhang Jing.,Yimin Hu.,...&Jiadong Li.(2024).BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision.International Journal of Computer Vision,132,1659-1684.
MLA Xin Zhao,et al."BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision".International Journal of Computer Vision 132(2024):1659-1684.
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